Hostname: page-component-797576ffbb-cx6qr Total loading time: 0 Render date: 2023-12-05T04:24:00.597Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "useRatesEcommerce": true } hasContentIssue false

Natural language processing in-and-for design research

Published online by Cambridge University Press:  08 August 2022

L. Siddharth
Engineering Product Development, Singapore University of Technology and Design, Singapore487372, Singapore
Lucienne Blessing
Engineering Product Development, Singapore University of Technology and Design, Singapore487372, Singapore
Jianxi Luo
Data-Driven Innovation Lab, Singapore University of Technology and Design, Singapore487372, Singapore
Rights & Permissions [Opens in a new window]


We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

Review Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
© The Author(s), 2022. Published by Cambridge University Press

1. Introduction

Several natural language schemes such as ontologies (Li and Ramani Reference Li and Ramani2007), controlled natural language descriptions (Chakrabarti et al. Reference Chakrabarti, Sarkar, Leelavathamma and Nataraju2005), documentation templates (Lee et al. Reference Lee, Kim, Huh, Cho, Park and Lee2013), argumentation approaches (Eng, Aurisicchio, and Bracewell Reference Eng, Aurisicchio and Bracewell2017), artefact representations (Sasajima et al. Reference Sasajima, Kitamura, Ikeda and Mizoguchi1996), process models (Gero and Kannengiesser Reference Gero and Kannengiesser2012) and function structures (Gericke and Eisenbart Reference Gericke and Eisenbart2017) have been adopted in design research to envisage, encode, evaluate and enhance the design process. While these schemes have significantly impacted the development of several knowledge-based applications in design research and practise, it was not until the development of computational (e.g., graphical processing units (GPUs), cloud computing services) and methodological (e.g., NLTK,Footnote 1 WordNetFootnote 2) infrastructures that these schemes were popularly utilised to process unstructured natural language text data and extract design knowledge from these. These infrastructures have led to the evolution of what is currently understood and recognised as a family of natural language processing (NLP) techniques.

A typical NLP methodology converts a text into a set of tokens such as meaningful terms, phrases and sentences that are often embedded as vectors for applying these to standard NLP tasks such as similarity measurement, topic extraction, clustering, classification, entity recognition, relation extraction and sentiment analysis. These tasks primarily rely upon prescriptive language tools (e.g., Stanford Dependency ParserFootnote 3), lexicon (e.g., ANEWFootnote 4) and descriptive language models (e.g., BERTFootnote 5).

The ability of NLP methodologies to process unstructured text opens several opportunities such as topic discovery (Liang et al. Reference Liang, Liu, Chen and Jiang2018), ontology extraction (Bouhana et al. Reference Bouhana, Zidi, Fekih, Chabchoub and Abed2015), document structuring (Morkos, Mathieson, and Summers Reference Morkos, Mathieson and Summers2014), search summarisation (Noh, Jo, and Lee Reference Noh, Jo and Lee2015), keyword recommendation (Zhang et al. Reference Zhang, Kwon, Kramer, Kim and Agogino2017) and text generation (Souza, Meireles, and Almeida Reference Souza, Meireles and Almeida2021), which enable design scholars and practitioners to support knowledge reuse (Li et al. Reference Li, Chen, Zheng, Jiang and Wang2021a), needs elicitation (Lin, Chi, and Hsieh Reference Lin, Chi and Hsieh2012), biomimicry (Shu Reference Shu2010; Selcuk and Avinc Reference Selcuk and Avinc2021) and emotion-driven design (Dong et al. Reference Dong, Zhu, Peng, Tian, Guo and Liu2021) in the design process. NLP has therefore become an imperative strand of design research, where the scholars have extensively proposed NLP-based tools, frameworks and methodologies that are aimed to assist the participants in the design process, who otherwise often rely upon organisational history and personal knowledge to make important decisions, for example, choosing a lubricant for shaft interface.

In this article, we review scholarly contributions that have applied as well as developed NLP techniques to process unstructured natural language text and thereby support the design process. Several motivations (as follows) have led to the effort of reviewing such contributions.

  • (i) To identify the methodological advancements that are necessary to bolster the performances of future NLP applications in-and-for design. For instance, the performances of parts-of-speech (POS) tagger and named entity recognition (NER) require significant improvement to process design documents. We have listed various possibilities of such methodological directions in Section 4.2.

  • (ii) To enhance theoretical understanding of the nature and role of natural language text in the design process. For example, it is still unclear as to which elements of design knowledge are necessary to be present in an artefact description so that it qualifies as adequate. We have asked several open questions along with necessary discussion to highlight such theoretical directions in Section 4.3.

  • (iii) To summarise a large body of NLP contributions into a single source. A variety of NLP applications to the design process are reported in journals outside the agreed scope of design research. Reviewing and summarising such contributions in this article could therefore be of importance. We have reviewed the contributions according to the type of text source in Section 3.

  • (iv) To create an NLP guide for developing applications to support the design process. For example, design methods like creating activity diagrams could be significantly benefited by NLP methodologies. We have indicated such cases in Section 4.1 using a design innovation process framework.

In line with the motivations described above, we adopt a heuristic approach (Section 2 and Appendix A) to retrieve 223 articles encompassing 32 academic journals. We review these articles in Section 3 according to the types of text sources and discuss these in Section 4 regarding applications and future directions.

2. Methodology

To retrieve the articles for our review, we use the Web of ScienceFootnote 6 portal, where we heuristically search the titles, abstracts and topics using a tentative set of keywords within design journals. Upon carrying out a frequency-based analysis of the preliminary results, we expand the keyword list as well as the set of design journals. We further expand our search to all journals that include NLP contributions to the design process. We then apply several filters and manually read through the titles, abstracts and full texts of a selected number of articles. In the end, we obtain 223 articles that we review in our work. We detail the search process in Appendix A. We have also uploaded the bibliometric data for all these articles on GitHub.Footnote 7

As shown in Table 1, the final set of papers is distributed across 32 journals. We have strategically chosen these journals such that these are primarily design-oriented and secondarily focused on general computer applications (e.g., Computers in Industry), artificial intelligence (e.g., Expert Systems with Applications) and technology related (e.g., World Patent Information). In addition, we have also included journals that focus on general design aspects such as ergonomics, requirements and safety.

Table 1. Article count w.r.t. journals

a Indicates the journals that we initially considered as those that fall within the scope of design.

As shown in the year-wise plot (Figure 1), there has been a steady increase in the number of contributions, which could be mainly due to the evolution of computational and methodological infrastructures.Footnote 8 While the contributions from the 1990s have been theoretically influential, the peak in the mid-2000s could be attributed to the popularity of biomimicry (Goel and Bhatta Reference Goel and Bhatta2004), ontologies (Romanowski and Nagi Reference Romanowski and Nagi2004), functional modelling (Bohm, Stone, and Szykman Reference Bohm, Stone and Szykman2005) and functional representation (Chandrasekaran Reference Chandrasekaran2005). Besides the year-wise plot, we report the 30 most frequent keywords as a word cloud in Figure 2, where we discard the generic keywords such as ‘design’ and ‘system’.

Figure 1. Article count w.r.t. the publication year. The data point at 2021 is applicable only until 19th September 2021.

Figure 2. Top 30 keywords w.r.t. frequency.

3. Review

In this section, we review the 223 articlesFootnote 9 thus selected using the methodology as described in Section 2 and Appendix A. To present the articles that we have reviewed, we considered the following categorisation schemes: 1) the types of natural language text data (e.g., internal reports, technical publications), 2) the types of NLP tasks (e.g., clustering, classification) and 3) the applications in the design process (e.g., brainstorming, problem formulation). Among these schemes, we adopt the types of text data because an NLP-based contribution is often associated with one text source data but combines a variety of NLP tasks and could be applied across different phases of the design process.

As shown in Figure 3, we map the categories of our scheme onto different phases of the design process as given in the model of the UK Design Business Council.Footnote 10 Among the types of text data sources as explained below, consumer opinions and technical publications are utilised in the design process, whereas the rest are generated in the design process.

  • The internal reports are usually generated in the deliver phase of the design process, where the concepts are embodied and detailed into prototypes. These sources of natural language text often include the knowledge of failures, situations, logs, instructions, etc.

  • The design concepts are generated during the develop phase, when the designers search, retrieve, associate and select concepts using various supports. The NLP contributions that we review under this category not only involve processing design concepts but also problem statements, keywords, supporting databases (e.g., AskNature), etc.

  • The discourse transcripts constitute the recorded communication such as speech transcripts and emails that are obtained from organisational data or think-aloud experiments. These sources need not capture the communication that is pertinent to a particular phase but the design process as a whole.

  • The technical publications that constitute patents, scientific articles and textbooks are considered external sources that are often utilised in the develop and deliver phases of the design process. Owing to the quality and quantity of text, these sources are best suited for the application of NLP tasks.

  • The consumer opinions are external sources that are available in the form of product reviews and social media posts. These sources are predominantly utilised in the discover phase of the design process when the designers understand the usage scenarios and extract user needs.

  • We categorise the miscellaneous contributions as ‘other sources’ that are not indicated in Figure 3.

Figure 3. Indication of natural language text data sources related to the design process, following the design process model from the UK Design Business Council.

3.1. Internal reports

Internal reports constitute over 80% of the knowledge in the industry (Ur-Rahman and Harding Reference Ur-Rahman and Harding2012) and are often present as product specifications, design rationale, design reports, drawing notes and logbooks (Li et al. Reference Li, Qin, Gao and Liu2014). Although conventional NLP methodologies like building classifiers (Sexton and Fuge Reference Sexton and Fuge2020) using internal reports are a recent phenomenon in design research, scholars have attempted to process internal reports and discover ontologies (Cavazza and Zweigenbaum Reference Cavazza and Zweigenbaum1994), since the early 90s.

Requirement extraction

Scholars initially aimed to extract design requirements as meaningful terms, phrases and segments from internal reports to reuse these in the design process. Such requirements shall also be derived from the past cases of failure in which violated constraints were recorded (Siddharth, Chakrabarti, and Ranganath Reference Siddharth, Chakrabarti and Ranganath2019a). As mentioned below, scholars initially encountered some challenges while extracting design requirements from internal reports.

Kott and Peasant (Reference Kott and Peasant1995, p. 94) observe that requirements in internal reports are incomplete, ambiguous, include inconsistent rationale and denote a wrong purpose. To mitigate some of these issues, they provide an example (1995, p. 103) as shown below to illustrate how lengthy requirements could be decomposed into short sentences.

The Loader shall provide the capability of handling HCU6/E pallets, ISO 40-foot containers, and Type V airdrop platforms. Loader shall be able to move forward with speed of at least 5 mph, the goal being 7 mph. An on-board maintenance diagnostic system shall be provided.

The Loader shall be able to perform the Loading function. The Load Type of the Loading function shall be any of: HCU6/E pallets, ISO 40-foot containers, and Type V airdrop platforms. The Loader shall be able to perform function Move Forward. The speed of Move Forward shall be at least 5 mph, the goal being 7 mph. The Loader shall include an On-Board Maintenance Diagnostic System.

Farley (Reference Farley2001, pp. 296, Reference Zhang, Luo, Li and Buis299) identifies that airtime faults (also called ‘snags’) include abbreviations (e.g., CHKD – checked, S0V – serviceable), acronyms, spelling errors (e.g., VLVE) and plural terms. While differing in structure and semantics (Kim, Bracewell, and Wallace Reference Kim, Bracewell and Wallace2007, p. 155), internal reports also include noisy terms (Menon, Tong, and Sathiyakeerthi Reference Menon, Tong and Sathiyakeerthi2005, p. 179), ‘plastic’ terms (e.g., ‘progress’, ‘planning’) and implicit phrases (e.g., ‘insufficient performance’) (Lough et al. Reference Lough, Van Wie, Stone and Tumer2009, p. 62). Kim, Bracewell, and Wallace (Reference Kim, Bracewell and Wallace2007, p. 162) suggest that acronyms (‘CNC’) and abbreviations (‘chkd’) shall be recognised in text using ontologies. To reduce ambiguity, Madhusudanan, Chakrabarti, and Gurumoorthy (Reference Madhusudanan, Chakrabarti and Gurumoorthy2016, p. 451) suggest that the anaphora (‘those’) shall be replaced with the corresponding entity in the previous sentence.

When co-ordination ambiguity exists in a sentence, for example, ‘slot widths and radii should conform to those of cutters’ (Kang et al. Reference Kang, Patil, Rangarajan, Moitra, Robinson, Jia and Dutta2019b, p. 2), it is unclear if the term ‘slot’ modifies ‘widths’ or ‘radii’. Here, Kang et al. (Reference Kang, Patil, Rangarajan, Moitra, Robinson, Jia and Dutta2019b, pp. 6, Reference Ameri, Kulvatunyou, Ivezic and Kaikhah7) suggest checking if the corresponding domain ontology includes (‘slot’, ‘hasProperty’, ‘radii’). To extract meaningful segments that are devoid of ambiguities, Madhusudanan, Chakrabarti, and Gurumoorthy (Reference Madhusudanan, Chakrabarti and Gurumoorthy2016, p. 452) measure coherence between sentences by integrating and extending WordNet-based similarity measures. To extract segments within a sentence, for example, ‘sharp corners should be avoided because they interfere with the metal flow’, Kang et al. (Reference Kang, Path, Rangarajan, Moitra, Jia, Robinson and Dutta2019a, p. 294) extract the italicised portion using domain concepts (e.g., corner) and attributes (e.g., isSharp). They also discard the unwanted portion using some rules (2019a, p. 295), for exasmple, the subordinate clause that occurs after a marker shall be discarded, except for ‘if’ or ‘unless’.

Ontology construction

To represent design rationale,Footnote 11 scholars have proposed a variety of prescriptive-generic ontologies (Ebrahimipour, Rezaie, and Shokravi Reference Ebrahimipour, Rezaie and Shokravi2010; Liu et al. Reference Liu, Liang, Kwong and Lee2010; Zhang et al. Reference Zhang, Luo, Li and Buis2013; Aurisicchio, Bracewell, and Hooey Reference Aurisicchio, Bracewell and Hooey2016; Siddharth, Chakrabarti, and Ranganath Reference Siddharth, Chakrabarti and Ranganath2019a) that build upon the fundamental idea of entity-relationship models (Taleb-Bendiab et al. Reference Tan, Wang, Yang, Chen, Huang, Sun and Liu1993). While generic ontologies are capable of capturing rationale from a variety of domains, the performances of these in terms of knowledge retrieval are expected to be low due to the level of abstraction. For example, a list of generic terms that represent ‘issue’ (Liu et al. Reference Liu, Liang, Kwong and Lee2010, p. 4) may not retrieve phrases that inherently or intricately communicate a design issue.

Domain-specific ontologies like QuenchML (Varde, Maniruzzaman, and Sisson Reference Varde, Maniruzzaman and Sisson2013) and Kodak Cover (Nanda et al. Reference Nanda, Thevenot, Simpson, Stone, Bohm and Shooter2007) overcome the limitations of generic ontologies while also being evolvable (Poggenpohl, Chayutsahakij, and Jeamsinkul Reference Poggenpohl, Chayutsahakij and Jeamsinkul2004), machine-readable (Biswas et al. Reference Biswas, Fenves, Shapiro and Sriram2008; Fenves et al. Reference Fenves, Foufou, Bock and Sriram2008) and semantically interoperable (Ding, Davies, and McMahon Reference Ding, Davies and McMahon2009). Scholars have therefore attempted to extract domain-specific ontologies from domain text sources.

Among domain-specific ontologies, Kim, Bracewell, and Wallace (Reference Kim, Bracewell and Wallace2007, p. 160) identify the following categories of relationships from aircraft engine repair notes: background, cause-effect, condition and contrast. Lough et al. (Reference Lough, Van Wie, Stone and Tumer2009, p. 33) understand from 117 risk statements that these are indicators of failure modes, performance, design and noise parameters. Using oil platform accident reports, Garcia, Ferraz, and Vivacqua (Reference Garcia, Ferraz and Vivacqua2009, pp. 430, 431) propose that concept relationships could be generalised as Is-a, Part-Of, Is-an-attribute-of, Causes, Time-Follows, Space-Follows and more. Hsiao et al. (Reference Hsiao, Ruffino, Malak, Tumer and Doolen2016, p. 147) populate 822 actions contained in 185 risk reports and identify that action could carry the attributes ‘purpose’ and ‘embodiment’, which are further categorised as ‘Approval’, ‘Gather_data’, ‘Coordinate’ and ‘Request’ (Hsiao et al. Reference Hsiao, Ruffino, Malak, Tumer and Doolen2016, p. 158).

Scholars have built ontologies by associating technical terms and segments using various similarity measures. Hiekata, Yamato, and Tsujimoto (Reference Hiekata, Yamato and Tsujimoto2010) use an existing ontology to associate word segments (component and malfunction) from 9604 shipyard surveyor reports. Lee et al. (Reference Lee, Kim, Huh, Cho, Park and Lee2013) mine the task data from shipbuilding transportation logs and cluster these using a variety of distances (e.g., Jaccard, Euclidean). Kang and Tucker (Reference Kang and Tucker2016) extract functions as topic vectors from 16 module descriptions (Pimmler and Eppinger Reference Pimmler and Eppinger1994) of an automotive control system. They propose that the cosine similarity between a pair of topic-vectors (function) quantifies the functional interaction between corresponding modules. Song et al. (Reference Song, Yoon, Lee and Park2017, pp. 265–269) construct a semantic network using iPhone Apps PlusFootnote 12 text data that includes 697 service documents indicating 66 feature elements and 95 feature keywords.

Arnarsson et al. (Reference Arnarsson, Frost, Gustavsson, Jirstrand and Malmqvist2021) use latent dirichlet allocation (LDA) to cluster the Doc2Vec-based embeddings of over 8000 Engineering Change Requests (ECRs) in a commercial vehicle manufacturer. Yang et al. (Reference Yang, Kim, Hur, Cho, Han and Seo2018) construct an ontology using 114,793 problem-solution records within preassembly reports inside an automotive manufacturer. They use the ontology to process (e.g., identify n-grams), structure and represent new text data in various forms (2018, p. 214) to facilitate the design and managerial decisions. Xu et al. (Reference Xu, Dang, Zhang and Chen2020) obtain the text data of 1844 problems and 1927 short-term remedies from a vehicle manufacturer. To link the problems and remedies, they transform the text using term frequency – inverse document frequency (TD-IDF) and perform K-means clustering for problems and short-term remedies, while also linking the clusters.

Design knowledge retrieval

While ‘knowledge retrieval’ could assume a broad meaning across different areas of research and practise, we mention this in reference to the methods that ‘retrieve’ terms, phrases and segments that include components, issues, constraints and interactions. The outputs of such retrieval methods shall be considered as ‘design knowledge’ if it is possible to re-represent these as <entity, relationship, entity> triples that form constituents of an artefact that is relevant to the design process.

For example, a segment extracted from a transistor patent (Saeroonter et al. Reference Saeroonter, Beak, Lee, Baeck, Shin, Jeyong and Dohyung2021, p. 8) – ‘an insulating material is deposited on the whole surface of the substrate having the first semiconductor layer’ shall be encoded into triples such as <insulating material, is deposited, whole surface>, <whole surface, of, substrate> and < substrate, having, first semi-conducted layer> that form the constituents of the patent that shall be utilised as knowledge aid in the design process. To extract such relevant terms, phrases and segments, the scholars have adopted a couple of directions. First, using an ontology that shares the same domain as target text data so that relevant portions of the text are identified. Second, indexing the unstructured text data using a classification algorithm so that the search is restricted to the relevant portions.

To assist with case-based reasoning, Guo, Peng, and Hu (Reference Guo, Peng and Hu2013) build a domain ontology using 1000 injection moulding cases that were encountered in a Shenzhen-based company. They demonstrate using an Information-Content (IC) based similarity measure as to how the ontology aids in knowledge retrieval. For case-based retrieval, Akmal, Shih, and Batres (Reference Akmal, Shih and Batres2014) compare a variety of ontology-based similarity measures (e.g., Tversky’s Index, Dice’s Co-efficient) against numeric similarity measures (e.g., Wu-Palmer, Lin) to observe that the former deviated less from expert’s similarity scores. To retrieve CAD models using text inputs, Jeon et al. (Reference Jeon, Lee, Hahm and Suh2016) demonstrate how ontologies could be used as intermediaries. To assist CAD designers with design rule recommendations, Huet et al. (Reference Huet, Pinquie, Veron, Mallet and Segonds2021) create a knowledge graph around a design rule using relationships such as ‘has keyword’ (semantic context), ‘has material’ (engineering context) and ‘has employee’ (social context).

To relate phenomena and failure modes, Wang et al. (Reference Wang, Wu, Liu and Gao2010) extract a lightweight ontology from 400 aviation engine failure analysis reports and utilise the ontology to represent phenomena and failure modes as attribute-value vectors. They (2010, pp. 270, 271) then map the phenomena and failure mode vectors using an artificial neural network. To extract candidate components and responsibilities from the design rationale text, Casamayor, Godoy, and Campo (Reference Casamayor, Godoy and Campo2012) obtain sentences from IBM supported rationale suiteFootnote 13 and the UNICEN university repository.Footnote 14 Upon classifying the sentences as functional or nonfunctional using a semi-supervised approach, they extract verb phrases as candidate responsibilities and group these using the hierarchical clustering method to identify candidate components.

To understand the coupling between design requirements, Morkos, Mathieson, and Summers (Reference Morkos, Mathieson and Summers2014, p. 142) construct a bipartite network of terms and 374 requirements obtained from Toho (160) and Pierburg (214) manufacturing projects. They label a portion of these terms as ‘useful’ or ‘not useful’ and vectorise these using the network properties (29 features) and string length (1 feature). They train a neural network using the labelled dataset to classify the rest of the terms. Using the set of terms that are classified as ‘useful’, they reconstruct the bipartite network and retrain the classifier until the length of the list of terms is saturated (Morkos, Mathieson, and Summers Reference Morkos, Mathieson and Summers2014, p. 149).

To classify and index airtime faults, Tanguy et al. (Reference Tanguy, Tulechki, Urieli, Hermann and Raynal2016) train a support vector machines (SVM) classifier on 136,861 labelled documents that were obtained from the French Aviation Regulator – DGAC. To classify the causes of automotive issues, Xu, Dang, and Munro (Reference Xu, Dang and Munro2018) obtain titles and descriptions of 2420 issues from a Chinese automotive manufacturer. They retrieve cause-related phrases using a domain ontology and label these with the categories of the Fishbone diagram – Man, Machine, Material, Method and Environment. They use the labelled dataset to train a binary-tree-based SVM classifier.

To identify computer-supported collaborative technologies, Brisco, Whitfield, and Grierson (Reference Brisco, Whitfield and Grierson2020, p. 65) obtain Global Design Project text data from 104 students and classify the sentences into requirements, technologies and technology functionalities using RapidMinerStudio.Footnote 15 Lester, Guerrero, and Burge (Reference Lester, Guerrero and Burge2020, pp. 133–135) classify the chrome bug reportsFootnote 16 into requirements, decisions and alternatives using the Naïve Bayes algorithm to find that features selected using optimisation approaches (e.g., Ant colony) result in higher F-1 measure compared to document characteristics (e.g., TF-IDF).

To index manufacturing rules, Ye and Lu (Reference Ye and Lu2020) train a feedforward neural network with two hidden layers (128 and 32 neurons) using the embeddings of manufacturing rules and eight category labels. Song et al. (Reference Song, Lee, Choi and Kim2020) train a Bi-directional LSTM using 350 building regulation sentences to extract predicates and arguments. For example, in a design rule – ‘The roof height of the building must be 15 meters or less,’ the predicate is ‘be less’ and the arguments are ‘roof height’, ‘building’and ‘15 meters.’ To automatically extract design requirements, Fantoni et al. (Reference Fantoni, Coli, Chiarello, Apreda, Dell’Orletta and Pratelli2021) process tender documents of Hitachi Railway using a variety of ontologies and classify a sentence as a requirement if it includes certain keywords.


We summarise the NLP methodologies applied to internal reports in Table 2 according to data, methods, and supporting materials. We indicate the future possibilities of these in bold font. We use the same table format to summarise the literature review for the remaining types of data sources as well. Internal reports mainly include issues and remedies that are pertinent to a specific organisation or a domain. Scholars have used a variety of internal reports to process, extract ontologies, and classify sentences. They have also demonstrated how ontologies are used for effective knowledge retrieval.

Table 2. Summary of NLP methodologies and future possibilities (bolded) with internal reports

We can observe from the data column of Table 2 that internal reports have been utilised from a variety of domains: Aerospace, Shipbuilding and Automotive. Surprisingly, none of the methodologies has utilised data sources from the most popular ‘silicon-based streams such as Integrated Circuits, Software Architecture and Data Structures. While discussion platforms like Stack Overflow and Reddit may not be classified as those included within internal reports, they include the knowledge of issues and solutions that are found both in the industry and academia.

Upon training nearly 0.8 million labelled patent documents for a classification task, Jiang et al. (Reference Jiang, Hu, Magee and Luo2022) observe that the accuracy tends to be higher when the input feature vectors integrate text, image and meta information of the document compared to only-text and only-image feature vectors. Hence, the analysis of mere text in multimodal documents like transportation logs (Lee et al. Reference Lee, Kim, Huh, Cho, Park and Lee2013) may not reflect the entire design knowledge that is being communicated in these.

As understood from the list of data sources, the accessibility to internal reports is highly restricted. Although analyses of internal reports have a high probability of extracting design knowledge, these sources are also characterised by low information content, for example, 350 building regulation sentences (Song et al. Reference Song, Lee, Choi and Kim2020). While this caveat limits the performances of classifiers, the ontologies extracted from these also may not be comprehensive. Hence, it is necessary to aggregate various internal reports from a domain into a single source of natural language text, for example, NASA Memorandum on Space Mechanisms – lessons learned (Shapiro et al. Reference Shapiro, Murray, Howarth and Fusaro1995).

As far as the methods are concerned, although scholars have applied several state-of-the-art methods to perform NLP tasks such as classification and clustering, they are yet to utilise language models like BERT. While such language models are expected to perform poorly on domain documents (Fantoni et al. Reference Fantoni, Coli, Chiarello, Apreda, Dell’Orletta and Pratelli2021), it would be significantly useful to develop domain-specific language models, for example, BioBERT (Lee et al. Reference Lee, Jeon, Ahn and Kwon2020). These models shall be useful for obtaining embeddings and subsequent tasks such as Named Entity Recognition and Text Classification. Apart from the cost and resource limitations, training these language models also requires high amounts of text data that does not seem currently feasible with internal reports.

Term identification is a fundamental NLP problem that has not been given enough attention by design scholars apart from those that have utilised internal reports. The terms like ‘roller bearing’ reduce the ambiguity caused by individual words ‘roller’ and ‘bearing’. Since meaningful terms are made of two or more words (Tseng, Lin, and Lin Reference Tseng, Lin and Lin2007; Fantoni et al. Reference Fantoni, Apreda, Dell’Orletta and Monge2013), it is critically important to identify these before applying higher-level NLP tasks. Scholars have resorted to ontology-based approaches to identify these terms (Yang et al. Reference Yang, Kim, Hur, Cho, Han and Seo2018; Fantoni et al. Reference Fantoni, Coli, Chiarello, Apreda, Dell’Orletta and Pratelli2021). While ontology-based approaches are recommended over common-sense lexicon (e.g., WordNet), it is necessary to rely on domain-specific language models and generic- design- and technical-oriented lexicon to identify general terms (e.g., rough surface). Although such supports are hard to build, there has been recent progress in the literature that adopts patent databases to develop a generic lexicon (Sarica, Luo, and Wood Reference Sarica, Luo and Wood2020; Jang, Jeong, and Yoon Reference Jang, Jeong and Yoon2021).

Term disambiguation is another fundamental NLP problem, for example, the terms such as ‘cathodic protection anode bed’, ‘deep anode well’ and ‘deep ground bed’ are often used to refer to ‘cathodic protection well’ (Xu and Cai Reference Xu and Cai2021, p. 5). Since the ambiguity posed by these terms concerns the underlying meaning, the approach to resolve this issue should concern the measurement of semantic similarities among these terms. Gu et al. (Reference Gu, Xu, Wu, Yang and Ye2005, p. 108) resolve semantic conflicts between similar sentences, for example, ‘I will buy a bike’ and ‘I will buy a bicycle’ using a WordNet-based ontology – FloDL. Such a type of semantic conflict resolution is hardly relevant to industrial applications. While usage of a common-sense lexicon like WordNet is not recommended for such tasks, it is necessary to obtain true embeddings of these terms using domain-specific language models so that cosine similarity reflects ‘nearly’ actual similarity.

3.2. Design concepts

Often associated with the develop phase of the design process (as indicated in Figure 3), design concepts are generated through search, retrieval, association and selection. The NLP methodologies applied to these stages need not use only concept descriptions as primary text sources but also the problems, keywords, source of stimuli, etc.

Concept search

To formulate a comprehensive set of keywords to search for concepts, researchers have sought WordNet for identifying troponyms (‘prevent’ ➔ ‘inhibit’) (Cheong et al. Reference Cheong, Chiu, Shu, Stone and McAdams2011, p. 3; Linsey, Markman, and Wood Reference Linsey, Markman and Wood2012, pp. 3, Reference Akmal, Shih and Batres4), bridge verbs (Chiu and Shu Reference Chiu and Shu2007, p. 50), semantically distant verbs (Chiu and Shu Reference Chiu and Shu2012, pp. 272, Reference Yan, Zanni-Merk, Cavallucci and Collet291) and morphological nouns (Lee, Mcadams, and Morris Reference Lee, Mcadams and Morris2017, p. 5). Chakrabarti et al. (Reference Chakrabarti, Sarkar, Leelavathamma and Nataraju2005, pp. 119–121) provide a systematic approach to searching and retrieving biological stimuli based on the SAPPhIREFootnote 17 model. In the Action construct of the SAPPhIRE model, for instance, they propose that the search could be a combination of verb, noun and adjective. Rosa, Cascini, and Baldussu (Reference Rosa, Cascini and Baldussu2015) build upon the approach of Chakrabarti et al. (Reference Chakrabarti, Sarkar, Leelavathamma and Nataraju2005) by combining SAPPhIRE and Function-Behaviour-Structure to form a unified ontology for biomimicry.

To effectively search for concepts of biological species, Rosa et al. (Reference Rosa, Rovida, Vigano and Razzetti2011) develop a structured database of these and group these using high-level functions that are represented using <verb, noun, predicate> where the predicate is represented as <preposition, noun>. Vandevenne et al. (Reference Vandevenne, Verhaegen, Dewulf and Duflou2015, pp. 21, 22) use the k-nearest neighbours (k-NN) algorithm to index the AskNatureFootnote 18 database by classifying 1531 unique analogical transfer strategies into the following levels (2015, p. 25): group (e.g., move or stay put, modify), subgroup (e.g., attach, adapt) and function (e.g., temperature, compression). Chen et al. (Reference Chen, Tao, Li, Liu, Li and Tang2021) examine 20 AskNature pages to extract meaningful keywords and structure–function knowledge using, respectively, TF-IDF values and selected dependency patterns.

The above-stated contributions aim to enhance the concept search w.r.t. the biological domain. We also review some approaches that sought other domains. To improve the quality of concepts generated by architects (Segers, de Vries, and Achten Reference Segers, de Vries and Achten2005), De Vries et al. (Reference De Vries, Jessurun, Segers and Achten2005) integrate WordNet-based word graphs and a sketching canvas. To assist novice designers to form domain-specific keywords, Lin, Chi, and Hsieh (Reference Lin, Chi and Hsieh2012, p. 356) map user needs and domain concepts through the so-called ‘OntoPassages’ that were extracted using a domain ontology, which was built using 111 documents belonging to the National Center for Research on Earthquake Engineering, Taiwan.

To recommend a suitable design method for a problem description, Fuge, Peters, and Agogino (Reference Fuge, Peters and Agogino2014) obtain 886 case study descriptions and method labels from human-centred design (HCD) Connect. They use latent semantic analysis (LSA) to obtain the vectors of the descriptions and train the following classifiers using the labelled dataset: Random Forest, SVM, Logistic Regression and Naïve Bayes. To enhance problem definition, Chen and Krishnamurthy (Reference Chen and Krishnamurthy2020) facilitate human-AI collaboration in completing problem formulation mind maps with the help of ConceptNet and the underlying relationships.

Concept retrieval

The contributions in this section are primarily retrieval systems that are built under the assumption that the problem is well defined, and the search keywords are known beforehand. Chou (Reference Chou2014) and Yan et al. (Reference Yan, Zanni-Merk, Cavallucci and Collet2014) adopt the Su-field problem modelling approach to systematically obtain ideas through TRIZ and manually evaluate these using a fuzzy-linguistic scale. Kim and Lee (Reference Kim and Lee2017) integrate various design-by-analogy approaches into an interface called Bionic MIR that allows retrieval of biological systems based on physical, biological and ecological relations.

In a tool named Retriever, for a search keyword (e.g., chair) and a relation (e.g., ‘is used for’) from ConceptNetFootnote 19 categories, Han et al. (Reference Han, Shi, Chen and Childs2018a, pp. 467–469) retrieve three re-representations (e.g., chair, bench, sofa) and corresponding entities (e.g., leading a meeting, growing plants, reading a book) that are connected by the selected relation. In another tool named Combinator, for the same inputs, Han et al. (Reference Han, Shi, Chen and Childs2018b, p. 12/34) retrieve the related entity (noun, verb, adjective) and concatenate it with the search keyword, for example, ‘Handbag’ ➔ ‘Origami Handbag’.

To support the rapid retrieval of concepts, Goucher-Lambert et al. (Reference Goucher-Lambert, Gyory, Kotovsky and Cagan2020) employ LSA on a design corpus to identify a near or far concept from the current concept. They then provide the concept thus identified from the corpus as a stimulus to the designers for generating more concepts. To demonstrate retrieval of concepts using the C-K theory (Hatchuel, Weil, and Le Masson Reference Hatchuel, Weil and Le Masson2013), Li et al. (Reference Li, Chen, Zheng, Wang, Jiang and Jiang2020) extract a healthcare knowledge graph by mining SVO tripes of the form: $ \underset{amod}{\underbrace{NP_{sub}}}\overset{nsubj}{\to}\underset{advmod}{\underbrace{VP}}\overset{dobj}{\to}\underset{amod}{\underbrace{NP_{obj}}} $ from 18,000 Chinese websites. They also populate an FBS-based ‘nursing bed’ knowledge graph using experts.

Concept association

Once the concepts are generated using the search and retrieval methods, it is necessary to group similar concepts, especially when a large number of concepts are crowdsourced. In this section, we review NLP contributions that associate concepts predominantly using graph-based approaches. Zhang et al. (Reference Zhang, Kwon, Kramer, Kim and Agogino2017, p. 2) group 930 concepts (described as paragraphs) that were obtained from a human-centred design courseFootnote 20 using Word2Vec and the hierarchical clustering algorithm. Ahmed and Fuge (Reference Ahmed and Fuge2018, p. 11,12/30) measure topic level association for 3918 ideas that were submitted to OpenIDEOFootnote 21 using a Topic Bison Measure, which indicates if a topic pair co-occurs in an idea as well as the proportions of the pair.

To examine the effectiveness of crowdsourced stimuli, Goucher-Lambert and Cagan (Reference Goucher-Lambert and Cagan2019) crowdsource concepts as three nouns and three verbs for 12 design problems and categorise these as near, far and medium stimuli based on the frequency and WordNet-based path similarity. He et al. (Reference He, Camburn, Liu, Luo, Yang and Wood2019) crowdsource text descriptions of thousands of ideas to future transportation systems via Amazon Mechanical Turk. They (2019, pp. 3, 4) form a coword network of these ideas and use MINRESFootnote 22 to extract core ideas from the network. Liu et al. (Reference Liu, Wang, Li and Liu2020, p. 6) summarise 1757 scientific articles (solutions to a transmission problem) by building Word2Vec-based semantic networks around the central keywords – {transmission, line, location, measurement, sensor and wave}. Camburn et al. (Reference Camburn, Arlitt, Anderson, Sanaei, Raviselam, Jensen and Wood2020a, Reference Camburn, He, Raviselvam, Luo and Wood2020b) utilise HDBSCANFootnote 23 for clustering crowdsourced concepts and TextRazorFootnote 24 for extracting entities and topics from these.

Concept selection

In this section, we review the contributions that have utilised NLP supports to evaluate and select concepts. These concepts primarily aim to measure one or more success metrics (e.g., novelty). Delin et al. (Reference Delin, Sharoff, Lillford and Barnes2007, pp. 125–129) use bipolar adjectives obtained from the British National Corpus (BNC) to rate concepts. Strug and Slusarczyk (Reference Strug and Slusarczyk2009) evaluate floor plan concepts using the frequently occurring patterns in the hypergraph representations of past floor plans. To understand the concept selection phenomenon, Dong et al. (Reference Dong, Sarkar, Yang and Honda2014) model the change of linguistic preferences using the Markov Process and calculate the transition probabilities. To calculate creativity, Gosnell and Miller (Reference Gosnell and Miller2015, pp. 4–6) tie 27 concepts with some adjectives and match these against the terms – ‘innovation’ and ‘feasibility’ using DISCO.Footnote 25

Chang and Chen (Reference Chang and Chen2015) obtain 108 ideas for future personal computers from DesignBoomFootnote 26 and mine the idea-related information using RapidMiner. They apply K-means clustering to group the ideas and Analytic Hierarchy Comparison to evaluate these. Siddharth, Madhusudanan, and Chakrabarti (Reference Siddharth, Madhusudanan and Chakrabarti2019b, pp. 3–5) measure the novelty of a concept by comparing it against all entries in a reference product database across SAPPhIRE constructs and using a WordNet-based similarity. To examine the success of ideas that were submitted to Kickstarter – a crowdfunding platform, Lee and Sohn (Reference Lee and Sohn2019) shortlist 595 ideas in the Software-Technology category. They apply LDA to extract the most important 50 topics from the text descriptions of these ideas. Using the 50 topics and the funding received by the ideas, they conduct a conjoint analysis to examine the contribution of a topic to the success of an idea (2019, pp. 107, 108).


As we have summarised in Table 3, the NLP contributions that are pertinent to design concepts assist concept search, retrieval, association and selection. Scholars have utilised a variety of knowledge bases to search and retrieve concepts, while also recommending novel ways to expand search keywords. Since crowdsourcing concepts have recently emerged as an alternative to traditional laboratory-based design studies, scholars have therefore found the need to associate and group the concepts for assessing these. The NLP applications to concept selection are still emerging as there exist many metrics and many ways to compute these.

Table 3. Summary of NLP methodologies and future possibilities (bolded) with design concepts.

While scholars have utilised both general and domain-specific text sources for searching concepts, it is also possible to explore more text sources such as Encyclopaedia and How and Stuff Works. One of the most consulted platforms – YouTube seems unexplored. Although being primarily a video-sharing platform, the descriptions, comments and captions on YouTube are still useful text sources for inspiration.

To retrieve suitable search keywords, in addition to NLP-centric approaches like dependency parsing and TF-IDF, it is necessary to construct design knowledge graphs for specific streams such as engineering, architecture and software. Such knowledge graphs are likely to recommend new terms as well as assist with text completion for queries. For example, if we begin to search for ‘bearing’ and next-word predictions are ‘lubricant’ and ‘load’, we could choose ‘bearing lubricant’ and leverage from next word predictions like ‘density’, ‘film’ and ‘material’. Common-sense knowledge graphs like Google (and YouTube) make predictions based on many senses of the word ‘bearing’ and do not return the words as we have indicated in the example.

WordNet and ConceptNet have been the main supporting pillars for concept search as well as retrieval, while generic ontologies such as FBS and SAPPhIRE have been utilised to largely channel the search and retrieval processes. Since creative concepts emerge from the marriage of diverse sets of domains, a common-sense lexicon like WordNet is still a preferable supporting material. Similarly, scholars can also use readily available search methods like Google APIs to retrieve results from sources such as YouTube and patent databases. However, while retrieving concepts from a domain-specific knowledge source, it makes sense to utilise the domain-specific ontologies for query formation.

Alongside ontologies, scholars could benefit from the embeddings of common-sense language models such as BERT and GPT-xFootnote 27 to obtain nearby search keywords, compare search results, etc. Since the concept search and retrieval are largely exploratory and preferably involve diverse domains, the usage of common-sense language models shall not limit the desired performance of the NLP applications. While the same applies to concept association as well, the scholars shall also utilise domain-specific language models if the design problem is quite domain-specific.

Concept selection involves one or more metrics such as novelty, value, feasibility, and so forth. Scholars could benefit from an affective lexicon to rate the design concepts and carry out systematic approaches to analyse and present the results. Since the theory behind these metrics is yet to be consolidated, the NLP applications are still in nascent stages. Scholars can only benefit from preliminary NLP tasks such as similarity measurement, frequency analysis and term retrieval to assist them with one or more steps in the concept selection process.

Design theorists could benefit from the NLP methods to examine how successful concepts are selected. For example, Arts, Hou, and Gomez (Reference Arts, Hou and Gomez2021) observe the causality between frequencies of unigrams, bigrams and trigrams and the likelihood of a patent getting an award, for example, Nobel, Lasker, Bower and A.M. Turing. Similarly, scholars could leverage the text descriptions of concepts that have been selected for awards like Red Dot (indicates novelty or surprise)and Malcolm Baldrige National Quality Award (indicates value). Moreover, to understand the actual value of a concept, scholars could also utilise the sales information. For instance, Argente et al. (Reference Argente, Baslandze, Hanley and Moreira2020) connect the number of product units sold from Nielsen’s Retail Scanner data with the ‘value’ of a patent.

3.3. Discourse transcripts

Design communication is often documented as discourse transcripts in protocol studies, think-aloud experiments and recorded design workshops. Starting with a design issue or a problem, designers communicate subissues, solutions, related artefacts, arguments and justifications. Identifying and analysing the set of concepts that arise during such communications allows scholars to reveal a variety of insights about the design process.

A sequence of closely-related concepts within a segment in discourse transcripts represents a period of coherent communication, which could affect the design outcomes in terms of success metrics such as novelty and feasibility (Dong Reference Dong2007). To identify such concepts, scholars have extracted nouns, phrases, segments and topics, and associated these using vector-based or corpus-based similarity measurement techniques. We review such NLP-based approaches that are currently preferable over traditional linkographs (Botta and Woodbury Reference Botta and Woodbury2013).

Concept identification

Scholars have adopted different approaches to extract key concepts such as topics, words, ontology-based entities and n-grams. Wasiak et al. (Reference Wasiak, Hicks, Newnes, Dong and Burrow2010, p. 58) analyse emails to discover topics such as functions, performance, features, operating environment, materials, manufacturing, cost and ergonomics. From the email exchanges in a traffic wave project, Lan, Liu, and Lu (Reference Lan, Liu and Lu2018, p. 7) map word-frequency vectors and topic vectors (tasks, timestamps, persons, organisations, locations, input/output, techniques/tools) using Deep Belief Network – DBN (Bengio Reference Bengio2009; Lan, Liu, and Feng Lu Reference Lan, Liu and Feng Lu2017).

Goepp et al. (Reference Goepp, Matta, Caillaud and Feugeas2019, p. 165) identify the following speech acts from email exchanges: Information, Explication, Evaluation, Description and Request. These speech acts were associated with a set of verbs, for example, ‘Explication’ was associated with ‘explain’ and ‘clarify’. To capture significant phrases that denote design changes, using the DTRS7 dataset, Ungureanu and Hartmann (Reference Ungureanu and Hartmann2021) extract n-grams (0 < n < 8) based on frequency analysis and examine how short terms progress to a variety of long terms; for example, ‘a little’ ➔ ‘a little bit bigger’, ‘a little splash of colour’ (2021, p. 12).

Design process characterisation

To characterise the design process, scholars have aggregated the concepts thus identified from discourse transcripts into a whole (e.g., a semantic network) and performed analyses as reviewed below.

To characterise coherence in design communication, Dong (Reference Dong2005, pp. 450, 451) obtain vector representations of emails and memos using LSA and measure the standard deviations of these w.r.t. their centroid (mean). A low standard deviation of the set of vector representations is considered to denote a high coherence in communication (Dong, Hill, and Agogino Reference Dong, Hill and Agogino2004, p. 381). In an alternative approach, Dong (Reference Dong2006, pp. 39, 40) identifies segments by linking noun sequences using lexical relationships obtained from WordNet.

Based on the word occurrences of design alternatives within a time interval, Ji, Yang, and Honda (Reference Ji, Yang and Honda2012) model the relationship between preferences using the Preference Transition Model and Utterance-Preference Model. Menning et al. (Reference Menning, Grasnick, Ewald, Dobrigkeit and Nicolai2018, pp. 139, 142) use cosine similarity between LSA vectors of consecutive discourse entities to measure coherence. To characterise the uncertainty of the design process, Kan and Gero (Reference Kan and Gero2018) measure the text entropy of the transcripts that were obtained from protocol studies.

Georgiev and Georgiev (Reference Georgiev and Georgiev2018) utilise 49 WordNet-based semantic similarity measures to build a noun-based semantic network of students’ and instructors’ conversations as given in the DTRS10 dataset. They plot the average semantic similarity, information content, polysemy and level of abstraction w.r.t. time for characterising the design communication. Casakin and Georgiev (Reference Casakin and Georgiev2021) train regression models to establish a relationship between these network properties and the following metrics of design outcomes: originality, feasibility, usability, creativity and value.


As we summarise in Table 4, the NLP applications built using discourse transcripts are limited in comparison with other types of text sources due to the least accessibility and information content. While emails do not strictly qualify as ‘transcriptions’ of design communication, the currently available data sources are mainly DTRS datasets. Scholars could additionally explore panel discussions, protocol studies and client interactions (e.g., architect and customer). The accessibility to such sources is crucial for the development of NLP applications regarding discourse transcripts.

Table 4. Summary of NLP methodologies and future possibilities (bolded) with discourse transcripts

Beyond the likelihood of obtaining one or more of these sources, the probability of extracting meaningful design knowledge from these is quite limited. For instance, the usage of frequent colloquial phrases like ‘sort of too big’ limits the possibility of applying NLP methods to these (Glock Reference Glock2009). Moreover, a transcription, unlike any text document, involves a timestamp associated with its parts. Several factors such as lack of context, poor grammar, colloquial language and time variation are beyond what state-of-the-art NLP could handle. Scholars could still conduct preliminary analyses as they have done so far in terms of segment identification and topic discovery. Such analyses could also be benefitted from common-sense language models because verbal communication involves many common-sense terms. If required, scholars may also utilise domain-specific ontologies to recognise the domain terms in their analyses.

3.4. Technical publications

Technical publications include over 92 million patents and a portion of over 174 million records that comprise textbooks, journal articles and conference proceedings.Footnote 28 Due to their coverage, size and accessibility, these sources carry a significant advantage over other sources in terms of knowledge aids for the design process. Moreover, since these sources are peer-reviewed and adhere to grammar and typographical norms, NLP tasks are well-suited to these (Wang, Lu, and Loh Reference Wang, Lu and Loh2015).

Patent documents

As mentioned in section ‘Design knowledge retrieval’, design knowledge that is extracted from the text could be in the form of terms, phrases and segments, that should represent one or more constituents of an artefact that is relevant to the design process. Moreover, if re-represented, such terms, phrases and segments of text must assume the form <entity, relationship, entity> to store these in a machine-readable form. Being a large body of technical inventions, patents offer a rich source of design knowledge that is also characterised by high information content, quality and technicality.

To extract design knowledge from patents, scholars have primarily utilised ontologies to channel their extraction approaches. To extract issue-related concepts and relationships (noun–noun, noun–adjectives), using a WordNet-based similarity, Liu et al. (Reference Liu, Liang, Kwong and Lee2010, pp. 4, 5) compare sentences in 46 patent abstracts against an ontology (list of terms) of issues, disadvantages and challenges. Moehrle and Gerken (Reference Moehrle and Gerken2012) use a domain ontology to extract bigrams and trigrams from 522 patents of SUBARU’s four-wheel drive. They use the terms thus extracted to measure patent–patent similarity using a variety of measures (2012, p. 817) such as Jaccard, Inclusion, Cosine and DSS-Jaccard.

Liang et al. (Reference Liang, Liu, Kwong and Lee2012) adopt a sentence graph approach and Issue-Solution-Artefact ontology to extract design rationale from 18,920 Inkjet Printer patents that were assigned to Hewlett–Packard (HP) and Epson. Using a similar dataset of inkjet printer patents, Liang et al. (Reference Liang, Liu, Chen and Jiang2018) develop the topic-sensitive content extraction (TSCE) model and verify the model by testing the effect of segment length, parameters, sample count and topic count. Fantoni et al. (Reference Fantoni, Apreda, Dell’Orletta and Monge2013) propose a heuristic approach to extract the terms that correspond to functions, behaviours and structures (FBS ontology) from patents. In function, for example, they consider the frequent combinations of verb–noun and verb–object.

To discover the structural form assumed by a collection of patents, Fu et al. (Reference Fu, Cagan, Kotovsky and Wood2013a) perform LSA on 100 randomly selected US patent documents. They consider only verbs (functions) and only nouns (surfaces) to perform two different LSAs and thereby obtain corresponding patent vectors. Using the cosine similarities, they discover the most optimal structural form – hierarchy using which they construct a patent network. They also label the clusters of patents with the closest terms (verbs or nouns).

Upon training the abstracts of 500,000 patents (CPC-F subsection) using Word2Vec, Hao, Zhao, and Yan (Reference Hao, Zhao and Yan2017) obtain the embeddings of 1700 function terms (e.g., grill, cascade) that are given by Murphy et al. (Reference Murphy, Fu, Otto, Yang, Jensen and Wood2014). They obtain a patent vector as a circular convolution ($ \otimes $) of function terms that are present in the corresponding patent abstract. To support efficient retrieval of patent images, Atherton et al. (Reference Atherton, Jiang, Harrison and Malizia2018, pp. 247, 248) annotate images in USPTO with geometric features and functional interactions extracted from claims. Song and Fu (Reference Song and Fu2019) obtain three patent-word matrices using 1060 patents and three sets of words corresponding to components, behaviours and materials. They apply a nonnegative matrix factorisation algorithm to these matrices to extract significant topics.

While patents offer design knowledge in specific domains, due to the totality of technology space covered by the patent database, scholars have also attempted to construct WordNet-equivalent lexicon as well as engineering ontologies. Vandevenne et al. (Reference Vandevenne, Verhaegen, Dewulf and Duflou2016, p. 86) analyse titles and abstracts in a randomly drawn set of 155,000 patents from the EPO databaseFootnote 29 to discover that nouns are abstract (e.g., system, device) and are meaning enablers (e.g., temperature, pressure) that also point towards the product (e.g., valve, display).

To identify the primary users of technological inventions that are documented as patents, Chiarello et al. (Reference Chiarello, Cimino, Fantoni and Dell’Orletta2018) extract a generic list of users in terms of job positions, sports, hobbies, animals, patients and others. They identify these generic users in selected patentsFootnote 30 using a semi-automatic approach and annotate the sentences using these. They then feed the annotated dataset of sentences into SVM and multilayer perceptron for named entity recognition (NER).

Sarica, Luo, and Wood (Reference Sarica, Luo and Wood2020) obtain embeddings of over 4 million unique terms from the titles and abstracts across the US patent database. Using a web-based tool called TechNet,Footnote 31 they facilitate a search for these terms (Sarica et al. Reference Sarica, Song, Luo and Wood2021) and utilise the embeddings of these to construct a similarity network (Sarica and Luo Reference Sarica and Luo2021). To create an engineering alternative to WordNet, Jang, Jeong, and Yoon (Reference Jang, Jeong and Yoon2021) collect 34,823 automotive patents (IPC B60). They examine the dependency patterns in abstracts and claim to extract dependency relations that form the TechWord network. For the words in the network, they create TechSynset by capturing the WordNet synonyms and calculating the cosine similarity between BERT-based embeddings of individual pairs.

Scholars have demonstrated how patents could act as stimuli for generating concepts as well as indirect supports for problem-solving through TRIZ-based tools (Cascini and Rissone Reference Cascini and Rissone2004; Vincent et al. Reference Vincent, Bogatyreva, Bogatyrev, Bowyer and Pahl2006; Zanni-Merk, Cavallucci, and Rousselot Reference Zanni-Merk, Cavallucci and Rousselot2009; Prickett and Aparicio Reference Prickett and Aparicio2012). In the effort to discover patent network structures, Fu et al. (Reference Fu, Cagan, Kotovsky and Wood2013a) include a design problem in their LSAs to identify a starting point for navigating the patent network. Given a starting point in the network, Fu et al. (Reference Fu, Chan, Cagan, Kotovsky, Schunn and Wood2013b) consider patents at one and three hops as ‘near’ and ‘far’, respectively. They examine the effect of ‘near’ and ‘far’ patents on novelty and quality when these patents are given stimuli alongside the design problem.

To support patent retrieval, Murphy et al. (Reference Murphy, Fu, Otto, Yang, Jensen and Wood2014) adopt a Zipfian statistic approach to extract 1700 function (verbs) terms from 65,000 patent documents and organise these into primary, secondary and tertiary w.r.t. the functional basis (Stone and Wood Reference Stone and Wood2000). They index 2,75,000 patents using these functions that also act as query elements. To map design problems and patents via the Functional Basis, Longfan et al. (Reference Longfan, Yana, Yan and Cavallucci2020) train a semi-supervised learning algorithm based on Naïve Bayes and E-M algorithm using 1666 patents and the texts labelled with function categories. In another approach, they extract meaningful terms from patents using a frequency-based statistic (2020, p. 8) and cluster the patents according to the terms.

Although several approaches to searching and managing patents exist (Russo and Montecchi Reference Russo and Montecchi2011; Montecchi, Russo, and Liu Reference Montecchi, Russo and Liu2013; Dirnberger Reference Dirnberger2016), it is necessary to simplify the patent documents before utilising these as stimuli for generating concepts. To form keyword summaries of patent search results, Noh, Jo, and Lee (Reference Noh, Jo and Lee2015) conduct an experimental study to find that it is best to extract 130 keywords from abstracts using TF–IDF and Boolean expression strategies.

Sarica et al (Reference Sarica, Song, Luo and Wood2021) propose TechNet (Sarica, Luo, and Wood Reference Sarica, Luo and Wood2020) as a means to search and expand technical terms, which were extracted from the titles and abstracts in the patent database. To facilitate cross-domain term retrieval, Luo, Sarica, and Wood (Reference Luo, Sarica and Wood2021) organise the output of TechNet into various domains that are associated with a knowledge distance measure. Souza, Meireles, and Almeida (Reference Souza, Meireles and Almeida2021) train an LSTM-based sequence-to-sequence (abstract-title ➔ summary) neural network using 7000 patents for generating abstract summaries of patent documents. They group the summaries thus generated using a semantic similarity measure (Al-Natsheh et al. Reference Al-Natsheh, Martinet, Muhlenbach and Zighed2017) and subsequently identify patent clusters.

Patents not only document technological inventions but are also assigned to specific domains, companies, inventors and countries. Using such meta-data, scholars have developed technology maps for exploring design opportunities. Jin, Jeong, and Yoon (Reference Jin, Jeong and Yoon2015) extract meaningful terms from patents and use bag-of-words (BOW) approach to create patent vectors that form a technology map. Trappey et al. (Reference Trappey, Trappey, Peng, Lin and Wang2014) adopt a similar approach to patents and clinical reports that concern dental implants. Altuntas, Erdogan, and Dereli (Reference Altuntas, Erdogan and Dereli2020) use the same dental implant patents and obtain vectors of these using the patent-class matrix. They cluster the patent vectors using the following methods: E-M algorithm, self-organising map and density-based method.

To explore new design opportunities as well as to aid in idea generation, Luo, Yan, and Wood (Reference Luo, Yan and Wood2017) develop a technology space map using all CPC 3-digit classes and the co-citation proximity measures among these. They implement the map using support called InnoGPSFootnote 32 which provides several interactive features that are analogous to Google Maps. The support tool mainly allows the users to position themselves on the technology map, identify the closest domains and navigate the technology space map. Luo et al. (Reference Luo, Song, Blessing and Wood2018) conduct an experimental study to demonstrate how the total technology space map is useful for exploring ‘white space’ design opportunities related to artificial neural networks and spherical rolling robots.

To identify new technology opportunities relating to carbon-fibre heating fabric, Russo, Spreafico, and Precorvi (Reference Russo, Spreafico and Precorvi2020) download 16,743 patents and extract Subject–Action–Object triples where the Subject is ‘heating fibre’. Assuming that Action represents a function, they mine dependency patterns to extract applications (e.g., ‘applied as’, ‘used for’) and requirements (e.g., ‘enhance…’, ‘un…ability’) pertinent to the heating fibre technology. To explore new technology opportunities using products, Lee et al. (Reference Lee, Yoon, Kim, Kim, Kim, So and Kang2020) use the patent–product databaseFootnote 33 developed at the Korea Institute of Science and Technology Information (KISTI). They extract Word2Vec embeddings for products and technologies to create an exploration map that allows the identification of technologies closer to products and vice-versa. They also propose 10 indices to assess the performance of technology exploration.

To identify technology opportunities in 3G that could be leveraged in 4G, Zhang and Yu (Reference Zhang and Yu2020) extract effect phrases from the corresponding patents using a Bi-LSTM with a conditional random field layer. They label the words in the sentences using {Begin, Inside, Other} of an effect phrase and feed the labelled data into the neural network. They combine the effect phrases in a patent using a weighted TF-IDF vector and use topic clustering to group the patents. Depending on the number of patents on each topic, they calculate the technology opportunity score (2020, pp. 560, 561).

Textbooks and handbooks

Several design studies support the notion that exploring concepts from distant domains could lead to novel design solutions. Adhering to this consensus, Shu and colleagues have conducted analyses on a biological textbook (Purves et al. Reference Purves, Sadava, Orians and Heller2003) to understand the characteristics that support bio-inspiration. Shu (Reference Shu2010, p. 510) understands that the textbook includes candidates for design-by-analogy, for example, (‘bacteria’, ‘fill’, ‘pores of clothes’) ➔ ‘prevent dirt’. Cheong et al. (Reference Cheong, Chiu, Shu, Stone and McAdams2011, pp. 4, 5) identify that in the text, domain and common verbs co-occur, for example, ‘received and converted or transduced’.

To capture causally related biological functions, Cheong and Shu (Reference Cheong and Shu2014, pp. 1–4) locate and extract pairs of enabler-enabled functions using syntactic rules, for example, ‘Lysozymes destroy bacteria to protect animals’. Upon searching in the same textbook, Lee, Mcadams, and Morris (Reference Lee, Mcadams and Morris2017, pp. 5–7) identify morphological nouns that co-occur with the keywords in a single paragraph. For every noun, they calculate a modified TF-IDF metric (2017, pp. 5, 6) for usage in LSA.

The following articles describe approaches to extracting design knowledge from published handbooks. Hsieh et al. (Reference Hsieh, Lin, Chi, Chou and Lin2011) mine the Table of Contents, Definitions and Index from an Earthquake Engineering Handbook to develop a domain ontology. Kestel et al. (Reference Kestel, Kuegler, Zirngibl, Schleich and Wartzack2019) apply several text mining steps to the published document that describes the standard procedure for simulation of multibolted joints (VDI 2230 Part 2). They extract structured data with specific attributes (e.g., part, contact, load, relation) from the text and utilise these to build ontologies that are integrated with finite element analysis (FEA) tools.

Richter, Ng, and Fallah (Reference Richter, Ng and Fallah2019) obtain the design standards and guidelines for landfilling in different provinces of Canada. They conduct word-frequency analyses using metrics such as Gunning-Fox Index and Lexical Density. Xu and Cai (Reference Xu and Cai2021) mine 300 sentences from the underground utility accommodation policies in the departments of transportation such as Indiana and Georgia. They use utility-product and spatial ontologies to process and label the terms in the sentences with seven categories (2021, p. 7). They examine the POS and category patterns in these sentences to extract hierarchical knowledge structures.

Scientific articles

Unlike patents and books, the overall motive behind processing scientific articles is unclear, mainly due to a limited number of contributions. We, therefore, review these contributions as follows by explicitly stating the purpose beforehand. To summarise engineering articles by discovering their micro-and macro-structures, Zhan, Liu, and Loh (Reference Zhan, Liu and Loh2011, pp. 5, 6) train Naïve Bayes and SVM classifiers by labelling 1425 sentences from 246 research articles into one of the four categories: background, contribution, methodology, results and conclusions.

To identify the sentences that could aid in bio-inspiration, Glier, McAdams, and Linsey (Reference Glier, McAdams and Linsey2014, pp. 5–7) represent sentences from five biology journals using a feature vector of 1869 terms and label these as ‘useful’ and ‘not useful’ for bio-inspiration. They feed the labelled dataset into the following classifiers: SVM, k-NN and Naïve Bayes. To build a bridge between biological and engineering domains and thus aid bio-inspiration, Vandevenne et al. (Reference Vandevenne, Verhaegen, Dewulf and Duflou2016, p. 82) map product and organism aspects upon processing 155,000 EPO patents and 8011 papers from the Journal of Experimental Biology.

To create a generic engineering ontology, Shi et al. (Reference Shi, Chen, Han and Childs2017, pp. 4–6) develop a large semantic network called B-Link by extracting and combining entities from technical websites and articles, respectively, using ScrapyFootnote 34 and Elsevier APIKey.Footnote 35 To understand the evolution of typology in design research, education and practise, Won and Park (Reference Won and Park2021) collect 222 termsFootnote 36 from over 300 documents that include design publications, abstracts and discover that these terms have evolved from being object-based to concept-based.

To understand the definitions of contemporary technologies such as Artificial Intelligence and Industry 4.0, Giordano, Chiarello, and Cervelli (Reference Giordano, Chiarello and Cervelli2021) identify these terms in the sentences of Elsevier-Scopus abstracts and filter the cases where the neighbour of these terms adhere to a pattern, for example, ‘defined as’, ‘refer to’ (2021, p. 10). They further analyse the frequencies of the constituents of these sentences so filtered. To understand the field of product-service systems (PSS), Rosa et al. (Reference Rosa, Wang, Stark and Rozenfeld2021) develop a concept map by analysing 29 articles relating to the design of PSS.


We have summarised the NLP contributions that use technical publications in Table 5. Due to high accessibility, information content, quality and technical density, technical publications have been quite popular sources for developing NLP applications. The methodologies have also adopted state-of-the-art NLP methods while also utilising domain ontologies wherever applicable. Therefore, a little could be commented about the potential gaps in these contributions.

Table 5. Summary of NLP methodologies and future possibilities (bolded) with technical publications

Scholars could invest more effort into scientific articles (including conference proceedings) as the literature on patent analyses is extant. In addition, scholars could also report more analyses on full texts of patent documents, as a majority of contributions are limited to titles, abstracts and claims. Scholars could leverage the wealth of knowledge in these sources to create ontologies and knowledge graphs both at the generic and domain-specific levels. As a part of knowledge graph extraction, relation extraction shall adopt a rule-based approach in patent documents as the language is consistent across the entire database. In scientific articles, however, relation extraction requires prior named entity recognition as well as relation label prediction algorithms. Scholars could also immix patent documents and scientific articles in a particular domain to develop a domain-specific graph extraction tool.

3.5. Consumer opinions

Available in plenty as a part of social media text and product reviews, consumer opinions are reflective of actual user experiences (Decker and Trusov Reference Decker and Trusov2010), product specifications, requirements and issues (Jin et al. Reference Jin, Liu, Ji and Kwong2019). Consumer opinions often include typographical errors (e.g., coooolll), alternative word forms (e.g., LOL), multilingual terms and grammatical errors. It is a challenge to remove symbols, hyperlinks, usernames, tags, artificially generated messages and misspelt words. Lim and Tucker (Reference Lim and Tucker2016, pp. 1, 2) posit that identifying product features in consumer opinions often involves challenges in term disambiguation (e.g., ‘researchers should really screen for this type of error’) and keyword recognition (‘… just as this court case is about to start, my iPhone battery is dying’).

To work around the above-mentioned issues, Tuarob and Tucker (Reference Tuarob and Tucker2015a) propose using Carnegie Mellon POS tagger that suits social media text. In addition, He et al. (Reference He, Camburn, Liu, Luo, Yang and Wood2019, p. 4) recommend using TextRazorFootnote 37 for identifying proper nouns like ‘Uber’ and ‘Manhattan’. While processing consumer opinions, Tuarob, Lim, and Tucker (Reference Tuarob, Lim and Tucker2018, p. 4) prefer not to perform stemming due to its negative effects on the performances of downstream NLP tasks. To improve the grammatical structure, Wang et al. (Reference Wang, Tian, Li, Wang, Barenji and Cheng2019a, pp. 456–458) suggest a few transformation rules, for example, sentence 1 (e.g., ‘very nice’) is prepended with subject and verb to obtain sentence 2 (e.g., ‘It is very nice’) if the former does not include these. In addition to these approaches to work around the issues with consumer opinions, scholars have incorporated distinct steps before performing sentiment analysis, capturing usage context and modelling user emotions.

Sentiment analysis

Sentiment analysis is an important application of NLP that uses ratings as well as an affective lexicon to determine the polarity and intensity of sentiment in a piece of text. The sentiment scores quantify the product favourability (Tuarob and Tucker Reference Tuarob and Tucker2015a, p. 5) and affective performances (Chang and Lee Reference Chang and Lee2018, pp. 450, 451). Obtaining true sentiment scores is often a challenge, given that 22.75% of a social media text is sarcastic (Tuarob, Lim, and Tucker Reference Tuarob, Lim and Tucker2018). In addition, Tuarob and Tucker (Reference Tuarob and Tucker2015b) identify that neutral words constitute over 53% and 48.6% of smartphone and automobile-related tweets. Since the sentiment score of a phrase may not often match that of a sentence, Chang and Lee (Reference Chang and Lee2018, p. 462) propose to adjust the sentiment score of a local context based on the polarity match with the whole sentence.

Sentiment analysis utilises product features (nouns) and sentiment indicators (adjectives, adverbs and verbs); for example, ‘The keyboard is fine but the keys are real slippery’ includes product features {keyboard, keys} and sentiment indicators {fine, slippery} (Tang et al. Reference Tang, Jin, Liu, Li and Zhang2019, pp. 1, 2). Sentiment analysis requires an affective lexicon like SentiWordnet (Baccianella, Esuli, and Sebastiani Reference Baccianella, Esuli and Sebastiani2010), Affective Space 2 (Cambria et al. 2015) and SenticNet 6 (Cambria et al. Reference Cambria, Li, Xing, Poria and Kwok2020). We review in the remainder of this section, the contributions that have conducted sentiment analyses on various design text sources.

Raghupathi et al. (Reference Raghupathi, Yannou, Farel and Poirson2015) compute sentiment scores of Home Theatre reviews from Twitter, Amazon and Flipkart using the SENTRAL algorithm and the dictionary of affect language (DAL). To predict sentiment scores, Zhou, Jiao, and Linsey (Reference Zhou, Jiao and Linsey2015, p. 4) feed a labelled dataset of Kindle Fire HD 7 reviews into the fuzzy-SVM algorithm along with a lexicon that is populated using ANEW (Bradley and Lang Reference Bradley and Lang1999). Jiang, Kwong, and Yung (Reference Jiang, Kwong and Yung2017, pp. 2, 4) extract nouns, adverbs, verbs and adjectives from electric iron reviews and utilise SentiWordNet (Baccianella, Esuli, and Sebastiani Reference Baccianella, Esuli and Sebastiani2010) to predict sentiment scores.

Zhou et al. (Reference Zhou, Jiao, Yang and Lei2017) compute sentiment scores of specific product features in Kindle Fire HD reviews using ANEW and classification based on a rough set. They augment the sentiment scores with a feature model that was constructed by extracting product features using ARM and combining these using WordNet-based similarity measures (e.g., Resnik, Leacock-Chodorow). Jiang et al. (Reference Jiang, Kwong, Park and Yu2018, p. 394) assess 1259 reviews of six compact cars using SemantriaFootnote 38 to obtain sentiment scores. Tuarob, Lim, and Tucker (Reference Tuarob, Lim and Tucker2018, pp. 6, 8) use TextBlobFootnote 39 to compute sentiment scores of tweets related to 27 smartphone models. They account for sarcasm using the analysis of a coword network, where nodes are ranked for likelihood, explicitness and relatedness.

Tang et al. (Reference Tang, Jin, Liu, Li and Zhang2019) develop the tag sentiment aspect (TSA) Model to extract topics and sentiment indicators simultaneously. They demonstrate the proposed TSA model using DSLR and laptop reviews (Jo and Oh Reference Jo and Oh2011). Sun et al. (Reference Sun, Niu, Yao and Yan2019) calculate sentiment scores of 500,000 phone reviews from ZolFootnote 40 upon capturing the co-occurrence of product features and sentiment indicators (adjectives, adverbs) within a sliding window. For sentiment analysis, Suryadi and Kim (Reference Suryadi and Kim2019, pp. 3, 4) feed the labelled embeddings of informative laptop and tablet Amazon-based reviews into the long–short term memory (LSTM) model (He et al. Reference He, Lee, Ng and Dahlmeier2018).

Sun et al. (Reference Sun, Guo, Shao and Rong2020) mine 98,700 reviews and product descriptions of Trumpchi GS4 and GS8 vehicles that are manufactured by the GAC group. They use TF-IDF and fastText (Joulin et al. Reference Joulin, Grave, Bojanowski and Mikolov2017) to compute sentiment scores and extract attributes from the text thus mined. Chiarello, Bonaccorsi, and Fantoni (Reference Chiarello, Bonaccorsi and Fantoni2020) extract 7,165,216 Twitter posts that appeared ahead of the launch of Xbox One X and New Nintendo 2DS XL to examine the effect of sentiment polarity of the social media activity on the success of these products. They label 6500 tweets relevant/irrelevant and build an SVM classifier. Upon classifying the tweets that are outside the training set, they obtain 66,796 relevant tweets and compute the sentiment scores of these using a specific lexicon (Chiarello, Fantoni, and Bonaccorsi Reference Chiarello, Fantoni and Bonaccorsi2017).

Gozuacik, Sakar, and Ozcan (Reference Gozuacik, Sakar and Ozcan2021) classify Google Glass tweets using a Deep Neural Network for sentiment polarity and opinion usefulness. They include bag-of-words and other embedding techniques for comparing the classification performances. They find using clustering analysis (2021, pp. 9–11) that among the useful opinions, negative ones denote issues and positive/neutral ones denote innovations. To identify sentiment indicators, Han and Moghaddam (Reference Han and Moghaddam2021a) collect 23,564 sneaker reviews and fine-tune BERT for a named entity recognition task with the following labels on each word in a sequence: background, sentiment, attribute and description.

Han and Moghaddam (Reference Han and Moghaddam2021b) extract product attributes of sneakers from catalogues and product descriptions and apply a rule-based approach to compute sentiment scores w.r.t. these attributes. Li et al. (Reference Li, Liu, Lu, Zhang, Li and Yu2021) identify groups of customers and attribute preferences by clustering the Word2Vec embeddings of 30,000 laptop reviews from JD.Footnote 41 They estimate the sentiment score using Microsoft’s Deep Structured Semantic Model and utilise these sentiment scores to develop a Kano map between customer groups and attribute preferences.

Extracting usage context

In this section, we review the contributions that capture usage context by examining the product features and their functioning in different environments (Jin, Ji, and Liu Reference Jin, Ji and Liu2014; Shu et al. Reference Shu, Srivastava, Chou and Lai2015; Hou et al. Reference Hou, Yannou, Leroy and Poirson2019). Park and Lee (Reference Park and Lee2011) extract consumer opinions of 135 mobile phone models from a review portal.Footnote 42 Upon analysing the opinion data using TextAnalyser 2.0, they mine the frequent product specifications, cluster the consumers and form product-specific networks.

Wang et al. (Reference Wang, Cai, Leung, Lau, Li and Min2014) label and group camera reviews from Amazon and NewEgg using the frequent keywords obtained from product descriptions. They extract the aspects from these reviews using Fine-Grained LDA and Unified Fine-Grained LDA. To relate engineering characteristics with consumer opinions, Jin, Ji, and Liu (Reference Jin, Ji and Liu2015) obtain 770 reviews of HP and Epson printers from Amazon to extract engineering characteristics using n-gram language models. To aid House-of-Quality construction, Ko (Reference Ko2015) relate consumer and design requirements using a 2-tuple fuzzy-linguistic approach.

To extract important product features, Jin, Ji, and Gu (Reference Jin, Ji and Gu2016) select the most representative sentences from 21,952 reviews on CNET using a greedy algorithm and verify these using information comparativeness, information representativeness and information diversity. To classify product reviews, Maalej et al. (Reference Maalej, Kurtanovic, Nabil and Stanik2016) procure over 1.2 million Smartphone Application reviews from the Apple AppStore and Google Play Store. They label the reviews according to four categories: bug report, feature request, user experience or rating and train the labelled dataset using Naïve Bayes, decision tree and maximum entropy algorithms, while also examining the effect of different approaches such as Bag of Words, Bigrams, Lemmatisation and Stop words.

To extract product usage, Park, Kim, and Baik (Reference Park, Kim and Baik2016, p. 4) learn feature ontology by measuring triples like ‘fabric + shrink’ using Wu and Palmer similarity (Wu and Palmer Reference Wu and Palmer1994) and merging with factual (e.g., ‘fabric + rayon’) and sentiment (e.g., ‘shirt + disappoint’) ontologies using a Fuzzy Formal Concept Analysis (FFCA) approach. They identify the relationships between triples using explicit causal conjunctions such as ‘so’, ‘due to’ and ‘because’ (2016, p. 6).

To disambiguate product reviews, Singh and Tucker (Reference Singh and Tucker2017) classify the Amazon review sentences (obtained using into function, form, behaviour, service and others using the following classifiers: Naïve Bayes, SVM, Decision Tree and IBk classifiers. To identify the type of design knowledge in a product review, Kurtanovic and Maalej ( Reference Kurtanović and Maalej2018) label 32,414 reviews of 52 Amazon Store Apps with the following concepts: Issue, Alternative, Criteria, Decision and Justification. They apply the labelled dataset to the following classification algorithms: Naïve Bayes, SVM, Logistic Regression, Decision Tree, Gaussian Process, Random Forest and Multilayer Perceptron.

To capture bigrams that represent the usage context of wearable technology products, Suryadi and Kim (Reference Suryadi and Kim2018, pp. 6, 7) combine noun-adjective pairs that co-occur in a hierarchical path in the dependency tree. They (2018, p. 8) group the embeddings of the noun–adjective combinations using $ X $-means clustering. In an extended work, Suryadi and Kim (Reference Suryadi and Kim2019, p. 7) identify bigrams that are noun–verb, noun–noun, while verbs end with a -ing (e.g., ‘web browsing’, ‘reading books’).

Hou et al. (Reference Hou, Yannou, Leroy and Poirson2019, p. 3) structure an affordance description as ‘Afford the ability to [action word] [action receiver] [perceived quality] [usage context]’. Based on the structure, they (2019, p. 5) extract perceived opposite qualities (e.g., low, high) from Kindle reviews to train an ordered logit regression model. An affordance $ i $ that supposedly has the perceived qualities $ {X}_i $ and $ {Y}_i $ is characterised according to their model by the coefficients $ {\alpha}_i\hskip0.35em \mathrm{and}\hskip0.35em {\beta}_i $ that are used to identify categories of Kano (Reference Kano1984) model: must be, performance, attractive, indifferent, reverse and questionable.

Zhou et al. (Reference Zhou, Ayoub, Xu and Yang2020) filter uninformative reviews of Amazon products such as Echo and Alexa, using a fastText classifier and extract topics from these using LDA. To estimate the importance of product attributes, Joung and Kim (Reference Joung and Kim2021) collect 33,779 smartphone reviews from Amazon. They identify product attributes using LDA and sentiment scores using IBM Watson. They estimate the importance of product features using k-optimal Deep Neural Networks that were designed using the SHAPFootnote 43 method.

Kansei engineering

Kansei engineering aims to support the emotion-driven design and involves the acquisition of emotional responses using bipolar adjectives such as ‘hot-cold’ and ‘unique-conventional’ (Gosnell and Miller Reference Gosnell and Miller2015), extracting descriptive adjectives such as ‘fresh’ and ‘appealing’ (Korpershoek et al. Reference Korpershoek, Kuyper, van der Werf and Bosker2010) and clustering these adjectives (Choi and Jun Reference Choi and Jun2007; Munoz and Tucker Reference Munoz and Tucker2016). The NLP contributions as we review in the remainder of this section involve developing emotion vocabulary, describing emotions of artefacts, modelling product features and emotions, and developing fuzzy-linguistic membership functions.

Scholars have proposed design-specific emotion vocabulary to characterise artefacts. Desmet (Reference Desmet2012, pp. 4, 5) proposes nine groups of 25 emotion types and representative emotion words within these. Chaklader and Parkinson (Reference Chaklader and Parkinson2017, pp. 2–4) examine 500 reviews of Bose SoundLink headphones to identify 29 cue terms (2017, p. 2) that reflect ergonomic comfort. Kim et al. (Reference Kim, Ko, Rhiu and Yun2019) identify 15 clusters of 4941 reviews of recliners from Amazon and extract the most frequent adjectives from these clusters.

Scholars have applied existing vocabulary to describe artefacts in their studies that we review as follows. Karlsson, Aronsson, and Svensson (Reference Karlsson, Aronsson and Svensson2003) use several adjectives to describe the interiors of BMW 318, Volvo S60, VW Bora and Audi A6 along the lines of the following factors: pleasantness, complexity, unity, enclosed-ness, potency, social status, affection and originality. To identify the extent of brand importance in the design process, Rasoulifar, Eckert, and Prudhomme (Reference Rasoulifar, Eckert and Prudhomme2014, pp. 144, 145) interview 30 designers about a Tecnifibre tennis bag. From the responses, they extract Kansei, design and brand concepts and organise these into a multiple domain matrix.

To characterise the affective qualities of electronic readers, Wodehouse et al. (Reference Wodehouse, Vasantha, Corney, Jagadeesan and MacLachlan2018, pp. 489–492) obtain descriptive adjectives of these from a survey on visual attractiveness. They use RAKEFootnote 44 to extract keyphrases (e.g., ‘prefer physical books’) from the patent documents relevant to electronic readers. They form feature vectors of electronic readers using descriptive adjectives and the key phrases to cluster these vectors using ClusterGrammer.Footnote 45

To compare affective performances of similar products, Liao, Tanner, and MacDonald (Reference Liao, Tanner and MacDonald2020, p. 5/18) ask survey participants to place eight wearable products on the quadrants of two graphs: comfortable versus like clothing and delightful versus like clothing. Upon placing the products, they also ask the participants to select a suitable emotional descriptor (2020, p. 8/18). Hu et al. (Reference Hu, Guo, Duffy, Ren and Yue2020) collect emotional responses of a flash drive regarding its colour, contour and shell material to discover the emotional dimensions via multifactor analysis. Using a case study on Toaster, Guo et al. (Reference Guo, Hu, Duffy, Shao and Ren2021) assess Kansei ratings of groups based on consensus and dominance.

Scholars have attempted to establish a relationship between emotional descriptors and product features. Using a dataset of seven interior designs of truck cabs, Zhou et al. (Reference Zhou, Jiao, Schaefer and Chen2010) adopt K-optimal rule discovery and Ordinary Least-Squares Regression to map design elements and affective descriptors. Upon obtaining participant data on CNC machine tools, Wang (Reference Wang2011) establish a relationship between abstract (e.g., ‘Rigid/Flexible’) and elementary (e.g., ‘Firm/Fragile’) Kansei words using Support Vector Regression and Artificial Neural Network.

Vieira et al. (Reference Vieira, Osorio, Mouta, Delgado, Portinha, Meireles and Santos2017) measure the actuation force, contact force, stroke and snap ratio for 11 keys in an in-vehicle rubber keypad. They ask participants to rate the performances of these keys using seven Kansei words (e.g., unpleasant/pleasant, smooth/hard, loose/stiff). They observe using regression models that a significant relationship exists between the aforementioned design parameters and the Kansei ratings. To predict the Kansei ratings from the features of a bottle design, Mele and Campana (Reference Mele and Campana2018) train a neural network with the following architecture: input layer with 14 design features (e.g., geometry, process, material), two hidden layers and an output layer with eight ratings to corresponding Kansei words (e.g., classic/trendy, masculine/feminine).

Misaka and Aoyama (Reference Misaka and Aoyama2018) obtain Kansei ratings of crack patterns on pottery surfaces using 50 adjectives. They use a neural network with one hidden layer to model the relationship between ratings and crack characteristics such as width, fineness and fluctuation. Upon mining 4459 Amazon reviews of 30 road bikes using WebHarvy,Footnote 46 Chiu and Lin (Reference Chiu and Lin2018) construct a functional model and a morphological matrix for six design elements (e.g., saddle, tread surface). They identify the 11 most frequent adjectives and group these into four semantic sets (overall impression, usability, riding experience and weight) and compute the corresponding semantic differentials. They run a linear regression using each semantic differential as a dependent variable and the six design elements as binary categorical variables.

So (Reference So2019) conducts a study that involves ranking 115 adjectives to obtain 12 design words and five emotion words. Using the resultant words, he performs factor analysis to discover the following dimensions: tool, novelty, energy, simplicity and emotion. Among these dimensions, he found that emotion was a significant predictor of design preferences via the following models: Linear Regression, Random Forest, Neural Network and Gradient Boosting Machine. For 1474 French Press coffee maker reviews on Amazon, El Dehaibi, Goodman, and MacDonald (Reference El Dehaibi, Goodman and MacDonald2019, pp. 4–6) use crowdsourced efforts to highlight phrases that indicate sustainability and obtain the corresponding degree of emotion. They train a logistic classifier to predict the DoE from highlighted phrases, while also using LDA to extract topics from these.

Wang et al. (Reference Wang, Wang, Li, Tian and Tsui2019b) propose rules to automatically label reviews with affective attributes (e.g., like–dislike, reliable–unreliable) based on the affective words contained in these. In an alternative approach to automatically labelling reviews, they build a classifier by manually labelling 900 reviews of 20 stuffed toys from Amazon and training the following models: k-NN, Classification and Regression Tree (CART), Multilayer Perceptron, DBN and LSTM. Jiang et al. (Reference Jiang, Kwong, Kremer and Park2019) extract hair dryer reviews from Amazon and estimate the predictability of product attributes (weight, heat, power, speed) upon minimum, maximum and average sentiment scores over four time periods.

Upon mining reviews and product specifications of 19 upper limb rehabilitation devices from Amazon and Alibaba, Shi and Peng (Reference Shi and Peng2021) connect these with 10 customer requirements (e.g., flexible wear, no smell) using WordNet-based similarity. For each customer requirement, they measure satisfaction using adjectives and adverbs in the reviews. They also identify the functional implementation through product specifications. Next, they fit a curve to establish a relationship between functional implementation and customer satisfaction.

Chen et al. (Reference Chen, Tao, Li, Liu, Li and Tang2021, pp. 84, 85) obtain 60 images of cockpit interior designs from the web and 20 emotional terms (about cockpit) from aircraft experts. They form the similarity matrix among these 20 terms using WordNet and cluster these into four emotional dimensions, which are used to rate each image as per the Likert scale (2021, pp. 90, 91). They train the following neural networks using the images labelled with an emotional degree: Radical Basis Function, Elman and General Regression.

Kansei attributes (Wang et al. Reference Wang, Li, Liu, Tian and Tsui2018, pp. 408, 409) and degrees (Wang et al. Reference Wang, Li, Liu, Tian and Tsui2018, p. 410) are abstractions of adjectives (affective characteristic) and adverbs (affective degree). Using the affective degrees obtained from surveys or text mining, scholars have attempted to model the linguistic membership functions of affective characteristics. Wang et al. (Reference Wang, Li, Liu, Tian and Tsui2018, p. 411) extract adjective–adverb combinations from McAuley’s datasetFootnote 47 and map these to corresponding Kansei attributes and degrees. Wang et al. (Reference Wang, Wu, Liu and Ye2021) map a variety of fuzzy-linguistic term sets (e.g., {‘none’, ‘very bad’, ‘bad’, ‘medium’, ‘good’, ‘very perfect’, ‘perfect’}) to their membership degrees using a trapezoidal asymmetric cloud model. Scholars have adopted similar approaches to model Kansei variables and their corresponding fuzzy membership functions, for example, USB flash drives (Chou Reference Chou2016) and hand-painted Kutani cups (Chanyachatchawan et al. Reference Chanyachatchawan, Yan, Sriboonchitta and Huynh2017).


We summarise the NLP contributions that use consumer opinions in Table 6. These sources have been quite popular alongside technical publications, given the extensive accessibility and high information content. However, consumer opinions are quite poor in terms of language quality, which, as discussed previously, poses negative effects on the performance of fundamental NLP tasks. Since prescriptive tools like NLTK do not work well on these sources, NLP scholars have been developing deep learning models to carry out fundamental tasks like POS tagging (Young et al. Reference Taleb-Bendiab, Oh, Sommerville and French2018).

Table 6. Summary of NLP methodologies and future possibilities (bolded) with consumer opinions

Scholars have applied state-of-the-art NLP methods for sentiment analysis and extraction of usage context. Although Kansei engineering only concerns emotional descriptors for artefacts, scholars have significantly advanced this area by relating with product features and developing fuzzy-linguistic models. While scholars could additionally explore the YouTube platform for a newer set of opinions, any advancement in NLP applications to consumer opinions, therefore, depends on the advancement in core NLP research.

The current NLP applications use state-of-the-art methods that can identify negative reviews, filter the less useful ones, extract significant topics and group similar reviews. Companies can hire human resources to conduct post hoc analyses and test the products and services under those conditions that the consumers had deemed to malfunction. Developing NLP applications to support such post hoc analyses may not carry scholarly merit as much as generating value for the industry.

Scholars could rather utilise detailed product reviews given by experts to extract design knowledge at various levels of abstraction (e.g., Function–Behaviour–Structure). Extracting such knowledge could be of value to discovering design opportunities and generating problem statements. Domain experts who provide such detailed reviews can identify fundamental issues with a concept that is embodied in the product. An expert mentions all specifications, various use cases, do’s/don’ts and estimated lifetime. In addition, an expert provides the reviews with necessary context that is often absent in consumer opinions. YouTube provides both expert reviews and consumer opinions on a single platform, which is currently underutilised by scholars.

3.6. Other sources

Function structures

Built upon traditional function structures (Pahl and Beitz Reference Pahl and Beitz1988; Hubka and Eder Reference Hubka and Eder1990), the functional basis developed by Stone and Wood (Reference Stone and Wood2000) constitutes functions (e.g., convert, distribute) and flows (e.g., solid material, mechanical energy). The functional basis led to the development of functional models for several products for over 184 electromechanical products and 6906 artefacts (Bohm, Stone, and Szykman Reference Bohm, Stone and Szykman2005). Due to its tremendous popularity, several scholars have attempted to apply and build upon the modelling technique. We review such contributions that are relevant to NLP.

Sridharan and Campbell (Reference Sridharan and Campbell2005, pp. 141, 143) propose several grammar rules to ensure consistency in functional models. For example, to the function ‘remove solid’, the secondary inflow – ‘mechanical energy’ and the outflow – ‘reaction force’ is added, while, the primary outflow is modified to ‘two solids’ (2005, pp. 145–147). Sangelkar and McAdams (Reference Sangelkar and McAdams2012) improve on functional models by including user activities obtained from ICFFootnote 48 to create action–function diagrams, which they use to compare typical and universal products (e.g., Box Cutter and Fiskars Rotary Cutter).

Sen, Summers, and Mocko (Reference Sen, Summers and Mocko2013) formalise function structures using a prescribed vocabulary for entities and relationships while also proposing several rules for the construction of flows. For example, Rule 14 states (2013, p. 6), ‘A Material flow can have one or more upstream flows, all of which must be of type Material’. Agyemang, Linsey, and Turner (Reference Agyemang, Linsey and Turner2017) propose several pruning rules to reduce uncertainty and improve consistency in modelling function structures. For example, Rule 8 states (2017, p. 504), ‘Remove all signal, sense, indicate, process, detect, measure, track and display functions’.

To assist with the construction of function structures, Gangopadhyay (Reference Gangopadhyay2001) develop the Augment Transition Network – ATN parser that detects the entities and conceptual dependencies upon providing a text input. To automatically construct functional models, Yamamoto et al. (Reference Yamamoto, Taura, Ohashi and Yamamoto2010) extract (noun, part of, noun) triples (e.g., ‘wheel of car’) using the ESPRESSO algorithm (Pantel and Pennacchiotti Reference Pantel and Pennacchiotti2006) and develop a tree structure, where nouns are replaced by adjacent verbs found in documents.

Wilschut et al. (Reference Wilschut, Etman, Rooda and Vogel2018, p. 535) extract functions from sentences that comply with a specific grammatical structure, for example, ‘Component x provides power p to component y’. Using Wikipedia articles on ‘machines’, Cheong et al. (Reference Cheong, Li, Cheung, Nogueira and Iorio2017, pp. 4, 5) obtain and classify Subject–Verb–Object (SVO) triples as functions and energy flows if objects and verbs match with secondary terms on a functional basis and their WordNet synonyms. Also, if the combined similarity (Jiang-Conrath and Word2Vec) between the object and ‘energy’ is greater than 2.9, they classify the object is classified as energy flow.


We review some purpose-specific classifiers that were built using miscellaneous sources of natural language text. To classify manufacturing concepts using the manufacturing capabilities, Sabbagh, Ameri, and Yoder (Reference Sabbagh, Ameri and Yoder2018) label the concepts (e.g., ‘annealing’, ‘hardening’) with capabilities (e.g., ‘highspeed machining’) using the data provided by 260 suppliers listed in ThomasNetFootnote 49 and a manufacturing thesaurus (Ameri et al. Reference Ameri, Kulvatunyou, Ivezic and Kaikhah2014). They train the labelled dataset using the following classifiers: Naïve Bayes, k-NN, Random Forest and SVM. Sabbagh and Ameri (Reference Sabbagh and Ameri2020) obtain LSA-based vectors of manufacturing concepts and cluster these using the manufacturing capability data – ThomasNet for 130 suppliers in heavy machining and complex machining.

To map technical competencies and performances, using the methods such as probabilistic latent semantic indexing (PLSI), nonnegative matrix factorisation (NNMF) and latent dirichlet allocation (LDA), Ball and Lewis (Reference Ball and Lewis2020) extract topics from two corpora: course descriptions and project descriptions of students who were enrolled in the capstone. For each topic and each student, either from course or project, they compute the aggregated score based on his/her grade. They subsequently map course and project vectors using the following methods: linear regression, decision tree, k-NN, support vector regression and ANN.

4. Discussion

In Section 3, we have reviewed and summarised NLP contributions according to the types of text sources. In the summary sections for each type of text source, we have indicated the method-wise and data-wise limitations, while also mentioning specific opportunities. In this section, we discuss how the NLP contributions thus reviewed could be applied in the design process and what are the potential future directions for the scholars who would contribute to NLP in-and-for design.

4.1. Applications

To provide a summary of the design applications that are currently supported by NLP, we utilise the integrated design innovation framework that was developed at the Singapore University of Technology and Design (SUTD). The frameworkFootnote 50 builds upon the double-diamond model of the UK Design Business Council and includes various design modules within each phase. The framework has been utilised to train practitioners from various domains who attend design thinking workshops at the university. Over 20 workshops are held every year – during each workshop (2–3 days), on average, five design innovation facilitators train over 50 practitioners on design thinking. It is important to note that the framework does not span the entirety of the design process, methods and underlying steps. For instance, the framework does not cover immersed spatial thinking (Rieuf et al. Reference Rieuf, Bouchard, Meyrueis and Omhover2017). We utilise this framework to set a boundary for our discussion and to identify the application gaps that could potentially lead to future research opportunities for design scholars.

We list the modules of the design innovation framework across each phase of the design process, as shown in Table 7. For these modules, we highlight (underlined) the specific steps that are being supported by NLP to indicate the steps that are yet to be supported. We also highlight (bolded), on some occasions, the steps as well as NLP applications that could be considered future opportunities. We could consider Table 7 as a minimal NLP guide for choosing a module or a step within a module to develop specific NLP-based supports. In future, as more NLP contributions are reported in the literature, we hope to extend this NLP guide using a comprehensive list of design methods like the Design Exchange.Footnote 51

Table 7. Applications of NLP in the design process. We highlight the currently supported steps (underlined) within the module and future opportunities (bolded)


The design innovation framework suggests, as shown in Table 7, that in the discover phase, interviews are conducted with potential users to extract needs and insights. Upon collecting user perceptions on specific usage scenarios, a user journey map is developed. Consumer opinions from e-commerce and social media platforms readily provide user profiles along with their ratings, usage and needs. While the steps in the discover modules are largely accomplished using sentiment analysis (Zhou et al. Reference Zhou, Jiao, Yang and Lei2017; Tuarob, Lim, and Tucker Reference Tuarob, Lim and Tucker2018) and usage context extraction (Maalej et al. Reference Maalej, Kurtanovic, Nabil and Stanik2016; Park, Kim, and Baik Reference Park, Kim and Baik2016), Kansei engineering methods capture user emotions for the presented usage scenarios (Vieira et al. Reference Vieira, Osorio, Mouta, Delgado, Portinha, Meireles and Santos2017; El Dehaibi, Goodman, and MacDonald Reference El Dehaibi, Goodman and MacDonald2019).

Kansei engineering also allows establishing a relationship between emotions and product features to predict their importance (Chen et al. Reference Chen, Yu, Chu, Chen and Yu2021; Shi and Peng Reference Shi and Peng2021). The design knowledge thus extracted from consumer opinions is often not sufficient to capture the user journey as the opinions lack enough context and detail. Some seeding information like user persona (Li et al. Reference Li, Liu, Lu, Zhang, Li and Yu2021), touchpoints and channels (Hou et al. Reference Hou, Yannou, Leroy and Poirson2019) could be extracted to initialise the user journey map, which could only be developed upon mining detailed expert reviews (Jin, Ji, and Gu Reference Jin, Ji and Gu2016) and conducting user studies (Misaka and Aoyama Reference Misaka and Aoyama2018).


In the define phase, the user needs are identified and grouped while capturing the user personas to develop activity diagrams. The data generated thus far is utilised to concretise design opportunities and create function structures that map needs to product functions. In terms of gathering needs and persona, the NLP supports remain the same as what was discussed in the discover phase. To develop activity diagrams, Sangelkar and McAdams (Reference Sangelkar and McAdams2012) provide partial support by mining association rules from the action-function diagrams.

While there is a need for NLP support in terms of text generation to create opportunity statements, some documentation guidelines have been proposed to structure the requirements such that these are suitable to perform NLP tasks (Moitra et al. Reference Moitra, Siu, Crapo, Durling, Li, Manolios, Meiners and McMillan2019; Kang et al. Reference Kang, Path, Rangarajan, Moitra, Jia, Robinson and Dutta2019a). The scholars have extensively invested in NLP approaches to map needs to functions (Murphy et al. Reference Murphy, Fu, Otto, Yang, Jensen and Wood2014; Vandevenne et al. Reference Vandevenne, Verhaegen, Dewulf and Duflou2015), generate functions (Fantoni et al. Reference Fantoni, Apreda, Dell’Orletta and Monge2013; Wilschut et al. Reference Wilschut, Etman, Rooda and Vogel2018) and develop function structures (Gangopadhyay Reference Gangopadhyay2001; Yamamoto et al. Reference Yamamoto, Taura, Ohashi and Yamamoto2010; Gericke and Eisenbart Reference Gericke and Eisenbart2017) as we have reviewed in this article.


The develop phase capitalises on the concretised needs, problem statements and function structures from the define phase to generate solutions using various approaches such as mind-map, 6-3-5 sketching and design-by-analogy. Supports have been developed regarding the mind maps to generate nodes (Chen and Krishnamurthy Reference Chen and Krishnamurthy2020) and organise these into categories (Camburn et al. Reference Camburn, Arlitt, Anderson, Sanaei, Raviselam, Jensen and Wood2020a). In the absence of user needs, scholars have proposed various approaches to initiate design opportunities from technology maps (Trappey et al. Reference Trappey, Trappey, Peng, Lin and Wang2014; Luo, Yan, and Wood Reference Luo, Yan and Wood2017) and biomimicry strategies (Vandevenne et al. Reference Vandevenne, Verhaegen, Dewulf and Duflou2016; Cao et al. Reference Cao, Sun, Tan, Zhang and Liu2021). The approaches to design opportunity identification could also lend themselves to widening strategies such as keyword expansion (Linsey, Markman, and Wood Reference Linsey, Markman and Wood2012; Sarica et al. Reference Sarica, Song, Luo and Wood2021) and concept exploration (Han et al. Reference Han, Shi, Chen and Childs2018b; Goucher-Lambert et al. Reference Goucher-Lambert, Gyory, Kotovsky and Cagan2020).

While 6-3-5 sketch is often overlooked by scholars in terms of NLP, it is possible to label the sketches using object recognition and image classification algorithms. While the label for such algorithms often tends to be abstract (e.g., man, animal), it is possible to retrieve specific and label-related terms using ontologies and context information (Akmal, Shih, and Batres Reference Akmal, Shih and Batres2014). The sketches are often annotated with titles, definitions and the flow of events. To reduce annotation time and make plausible annotations, it is possible to use text generation approaches, especially sentence completion algorithms. In digital sketching interfaces, definitions of components (retrieved from knowledge bases) may pop up on hover.

Scholars have extensively contributed to the research in design-by-analogy in terms of identifying search keywords (Cheong et al. Reference Cheong, Chiu, Shu, Stone and McAdams2011; Lee, Mcadams, and Morris Reference Lee, Mcadams and Morris2017), generating solutions (Verhaegen et al. Reference Verhaegen, Peeters, Vandevenne, Dewulf and Duflou2011; Goel et al. Reference Goel, Vattam, Wiltgen and Helms2012; Fu et al. Reference Fu, Chan, Cagan, Kotovsky, Schunn and Wood2013b), especially via relation-based retrieval algorithms (Kim and Lee Reference Kim and Lee2017; Han et al. Reference Han, Shi, Chen and Childs2018a). These supports, however, inform less whether the analogies are suitable. The analogical inferences are therefore yet to be supported.

The design innovation framework shown in Table 7 suggests that the solutions thus generated should be gathered and checked for reality, novelty and value. The NLP contributions have been effective in associating and discovering categories among several crowdsourced solutions (Liu et al. Reference Liu, Wang, Li and Liu2020; Zhang et al. Reference Zhang, Liu, Wei, Tao, Li and Liu2017). While several other performance indicators such as flexibility and manufacturability are also important metrics to be considered while selecting concepts, computing value is difficult while developing a concept, as value requires sufficient usage context. Current NLP contributions are capable of supporting interim tasks in novelty assessment that is carried out in many ways (Ranjan, Siddharth, and Chakrabarti Reference Ranjan, Siddharth and Chakrabarti2018).


To deliver the solutions, the framework suggests creating a multimedia storyboard that communicates the role of solutions in specific scenarios. Scholars have proposed approaches to identify generic users and usage context from consumer opinions that could stimulate ideas for storyboarding. Object-detection algorithms coupled with knowledge graphs (Wan et al. Reference Wan, Liang, Du, Liu, Ou, Wang, Pan and Zeng2021) could be useful for labelling and describing scenes like storyboards. Kansei engineering methods could be adopted to capture emotional feedback on the storyboards. Besides multimedia storyboarding, NLP techniques could provide direct as well as indirect support for prototyping and developing scaled models.

To build, test and analyse prototypes, the current NLP supports help understand requirements including dependencies (Morkos, Mathieson, and Summers Reference Morkos, Mathieson and Summers2014), elicit requirements (Kott and Peasant Reference Kott and Peasant1995), capture design rationale (Liu et al. Reference Liu, Liang, Kwong and Lee2010; Deken et al. Reference Deken, Kleinsmann, Aurisicchio, Lauche and Bracewell2012), analyse failures (Ebrahimipour, Rezaie, and Shokravi Reference Ebrahimipour, Rezaie and Shokravi2010; Wang et al. Reference Wang, Wu, Liu and Gao2010) and facilitate case-based reasoning (Guo, Peng, and Hu Reference Guo, Peng and Hu2013; Akmal, Shih, and Batres Reference Akmal, Shih and Batres2014). While these existing supports are applicable for testing scaled models as well, building a scaled model requires dimensional analysis that maps the key design parameters (e.g., viscosity) onto the performance parameters (e.g., energy consumption).

As an alternative to dimensional analysis, scholars have adopted deep learning approaches to associate design and performance parameters. For example, upon combining three datasets,Footnote 52 Robinson et al. (Reference Robinson, Dilkina, Hubbs, Zhang, Guhathakurta, Brown and Pendyala2017) map building features such as area, number of floors, heating degree days and building activity onto the annual energy consumption using several models such as gradient boost, multilayer perceptron, KNN and SVR (2017, p. 894). While performance parameters like energy consumption are largely derived from industry standards, the influential design parameters could be chosen and evaluated based on Kansei methods (Vieira et al. Reference Vieira, Osorio, Mouta, Delgado, Portinha, Meireles and Santos2017; Misaka and Aoyama Reference Misaka and Aoyama2018).

4.2. Methodological directions

Based on our review, we propose eight methodological directions for future NLP applications to support the design process.

First, we prioritise the extraction of knowledge graphs from text, which will be utilised in the design process as a knowledge base. Second, we recommend the development and utilisation of domain-specific language models to perform tasks such as classification, NER and question-answering. In the third and fourth directions, we propose the development of one or more text generation and neural machine translation models. Next, we propose the adoption of NER methods and collaborative tagging approaches to facilitate the tasks such as classification and relation extraction. Further, we propose that scholars develop standard datasets using design text as a common evaluation platform for future NLP applications. Finally, we propose to develop success metrics for evaluating the efficacy of NLP supports.

We have listed these directions along with examples in Table 8. We provide specific examples for the first six directions using a publicly available text.Footnote 53 For the remainder of this section, we explain these directions in individual subsections.

Table 8. Methodological directions with examples

Design knowledge graph

A knowledge graph comprises facts of the form – $ \left\{\left\langle h,r,t\right\rangle \right\} $ and serves as an infrastructure for the development of various NLP applications. A design knowledge graph includes facts like <‘stapler’, ‘comprises’, ‘leaf spring’>, < ‘hammer’, ‘push’, ‘staple’ > that could be utilised or generated in the design process. A design knowledge graph carries informative as well as reasoning advantages over networks (Han et al. Reference Han, Sarica, Shi and Luo2021) that provide pairwise statistical (Sarica, Luo, and Wood Reference Sarica, Luo and Wood2020), semantic (Casakin and Georgiev Reference Casakin and Georgiev2021) and syntactic (Jang, Jeong, and Yoon Reference Jang, Jeong and Yoon2021) relationships among a large collection of design terms (lexicon).

To process and recognise entities in text sources like internal reports, design concepts and consumer opinions (Li et al. Reference Li, Lyu, Wang, Chen and Zheng2021b), it is necessary to build design knowledge graphs that could replace the common-sense lexicon (e.g., WordNet). Domain-specific ontologies (e.g., QuenchML) capture design knowledge using relationships ($ r $) such as ‘hasProperty’, ‘partOf’ and ‘hasWeight’ that are technically preferable in comparison with that of common-sense ontologies like ConceptNet, that is, the relationships such as ‘atLocation’ and ‘usedFor’ captured by these. However, domain-specific ontologies capture abstractions (e.g., <Component, hasWeight, xx>) rather than facts (e.g., <clamp, weighs, 65 grams>) that are extracted from natural language text and captured using knowledge graphs.

We have shown an example in Table 8 for the facts that could be extracted from a sample text. As discussed in Section 3.4.4, technical publications that include patents and scientific articles are preferable sources for extracting facts and developing design knowledge graphs due to their high accessibility, information content and quality. Scholars have indicated the possibility of extracting triples from the patent text (Soo et al. Reference Soo, Lin, Yang, Lin and Cheng2006; Cascini and Zini Reference Cascini, Zini and Cascini2008; Korobkin et al. Reference Korobkin, Fomenkov, Kravets, Kolesnikov and Dykov2015). Siddharth et al. (Reference Siddharth, Blessing, Wood and Luo2021), for example, apply some rules to extract facts from patent claims by exploiting the syntactic and lexical properties. While patents could offer rule-based extraction methods due to consistent language, scientific articles require a mix of rule-based, ontology-based and supervised approaches.

Domain-specific language model

Early models of language given by traditional grammar have often proposed a restricted set of rules for forming sentences (Chomsky Reference Chomsky2014, pp. 5, 6), which limits the opportunity to produce a vast number of sentences. The modern view of a language model involves training large corpora to capture the likelihood of a given sequence of words (or tokens), for example, ‘metallic bond is strong’ in the same order. Originally developed as N-gram models, these models have evolved into deep learning-based models or transformers such as BERT and GPT-x. These models advance the theory of acquisition model of a language (Chomsky Reference Chomsky2014, p. 38) via statistical embeddings of generative grammar, which is otherwise represented as parts of speech and structural dependencies.

These models capture the embeddings of tokens and sequences through masked language modelling where a large number of sequence–sequence pairs are provided as training data. The input–output pair must belong to the same sequence but nearly 15% of the input tokens are expected to be masked. The embeddings that result from these models could be directly used to train classifiers, sequence-to-sequence tasks like Q & A and NER tasks. Several variants of BERT have been introduced at the corpora level, for example, BioBERT (Lee et al. Reference Lee, Jeon, Ahn and Kwon2020) and at the architecture level, for example, k-BERT (Liu et al. Reference Liu, Zhou, Zhao, Wang, Ju, Deng and Wang2019). The variant k-BERT, for instance, stitches facts from a domain knowledge graph onto the tokens for training the model.

Using domain-knowledge embedded language models like k-BERT provides embeddings of terms that are meaningful. As opposed to common techniques such as BOW, LSA and Word2Vec, embeddings from domain-specific language models should return ‘nearly true’ cosine similarity between a pair of artefacts (described using text) that have domain-association, similar physical properties and perform similar functions. Moreover, such domain-specific embeddings could aid in efficient concept retrieval in the respective domain. For example, a radiology-specific language model should identify the terms closest to ‘Magnetic Resonance Imaging’ than a common-sense language model.

Text generation

Originally referred to as natural language generation (NLG) systems, for example, the DOCSY model (Andersen and Munch Reference Andersen and Munch1991), applications that generate text reduce cost, ensure consistency and maintain the standard of documentation (Reiter, Mellish, and Levine Reference Reiter, Mellish and Levine1995, pp. 261–265). Such applications are relevant to the design process where requirements must be elicited, opportunity statements must be generated and solutions must be described. In Table 8, we indicate an example where a seeding term ‘nano-ceramic coating’ results in plausible sentences using text generation algorithms.

To support ontology-based verification of requirements, Moitra et al. (Reference Moitra, Siu, Crapo, Durling, Li, Manolios, Meiners and McMillan2019, p. 347) propose that a requirement shall be expressed as follows: REQUIREMENT R (name); SYSTEM shall set $ x $ of $ X $ to $ {x}_1 $ (conclusion); when $ y\hskip0.35em \in \hskip0.35em Y $ (condition). Likewise, scholars have proposed templates for describing design concepts as well (Siddharth and Chakrabarti Reference Siddharth and Chakrabarti2018; He et al. Reference He, Camburn, Liu, Luo, Yang and Wood2019; Luo, Sarica, and Wood Reference Luo, Sarica and Wood2021). While such a template-based approach works with a limited scope, it is necessary to implement text generation algorithms that are built out of RNNs, LSTM and Transformers.

Zhu and Luo (Reference Zhu and Luo2021) fine-tune GPT-2 for mapping the problems (including categories) to solutions using problem-solution data obtained from RedDot.Footnote 54 They also explore the capabilities of GPT-3 that support analogy-by-design in terms of generating text descriptions upon providing source-target domain labels as inputs. For a given technology domain, using KeyBERT,Footnote 55 Zhu and Luo (Reference Zhu and Luo2022) extract topics (terms and keyphrases) from patent titles and create a dataset of topic-title pairs. They fine-tune GPT-2 for mapping topics to titles so that solutions (as hypothetical titles) could be generated using search keywords (topics of interest).

Neural machine translation

Neural machine translation (NMT) models are trained to map sequence-to-sequence using an encoder–decoder framework (Tan et al. Reference Tan, Wang, Yang, Chen, Huang, Sun and Liu2020). These models are often associated with Transformers owing to the similarity in structure and behaviour of the neural networks that were built to accomplish the mapping task. NMT models have been specifically built to perform cross-language translation tasks and these are useful to increase semantic interoperability in design environments. For example, the rules ‘Smith Ltd shares machines with NZ-based companies’ and ‘Smith Ltd allows NZ-based companies to use its machines’ mean the same but are written in different forms (Ye and Lu Reference Ye and Lu2020).

In Table 8, we have shown an example of semantic forms that could be mapped from design text through neural machine translation. To standardise manufacturing rules, Ye and Lu (Reference Ye and Lu2020) map a manufacturing rule into a semantic rule using a neural machine translation model (Luong, Pham, and Manning Reference Luong, Pham and Manning2015) that comprises an encoder and a decoder with 256 gated-recurrent units (GRUs) present in each (Ye and Lu Reference Ye and Lu2020). Chen et al. (Reference Chen, Chen, Liu and Ye2020) propose semantic rule templates to formalise requirements so that these are easily verified using ontologies. NMT models coupled with semantic rule templates are necessary to translate ambiguous natural language sentences into a machine-readable form.

Named entity recognition

NER is a sequence-to-sequence task like POS tagging where entities and their respective tags are identified, for example, ‘General Electric’ as an organisation and ‘San Francisco’ as a location. From a design perspective, the term ‘fan’ shall be recognised as a product and the terms ‘ceiling fan’, ‘exhaust fan’and ‘CPU cooling fan’ shall be recognised with specific categories. While plenty of NER models and associated datasets exist for common-sense entity recognition (Yu, Bohnet, and Poesio Reference Young, Hazarika, Poria and Cambria2020), design-based datasets and models are yet to evolve. NER is also the first step towards the extraction of knowledge graphs, as described in Section 4.2.1.

In Table 8, we have shown that in a given design text, entities like ‘nano-ceramic coating’ and ‘solid quartz’ must be identified using tags like coating material and coating solution. Before the identification of entity tags, it is necessary to recognise terms that comprise one or more words (n-grams). Scholars have often utilised POS tags, dependencies and ontologies to recognise n-grams. Due to poor performance, such approaches must be replaced with deep learning models, as demonstrated by Chiarello et al. (Reference Chiarello, Cimino, Fantoni and Dell’Orletta2018) in their NER application.

Collaborative tagging system

Collaborative tagging (or folksonomy) is useful for the classification of a large set of documents as well as sentences in these. This bottom-up approach has been recently popular instead of a traditional top-down approach where the classification scheme is defined by the experts, for example, International Patent Classification. The current classifications in vast knowledge sources like Patent Databases, Web of Science and Encyclopaedia are less useful for developing NLP applications to support the design process. For instance, the classification codes that are assigned to a patent could inform the type of invention but not its purpose, behaviour and components.

We have indicated an example in Table 8 for the design-specific tags that could be assigned to individual sentences in a text document. The tags shall be recommended based on external knowledge as well as the previous tags (Hsieh et al. Reference Hsieh, Stu, Chen and Chou2009). These tags could also be expanded using classifiers (Sexton and Fuge Reference Sexton and Fuge2020). While several advantages to collaborative tagging exist, scholars are yet to introduce or develop many interfaces that help to assign tags to documents that are universally accessible. COIN platform is an example of such a collaborative tagging system (Panchal and Messer Reference Panchal and Messer2011). The use of such interfaces in design education, workshops and laboratory settings allows a variety of tags to be assigned to an open-source document that could be reused for developing retrieval algorithms.

Standard datasets

None of the NLP contributions that we have reviewed in this article leverage a design-specific gold standard dataset for evaluation. If an embedding technique is used for measuring the similarity between text descriptions of two artefacts, what is the trueness of that similarity? Similarly, if an application combines several tasks like NER and classification, to extract FBS from text, what is the efficacy of the application? For such cases, scholars are currently creating their datasets from scratch, which reduces the possibility of comparing different applications within design research.

A gold standard dataset is necessary for NLP applications that aim to measure artefact level metrics such as novelty, feasibility and so forth. These metrics shall be measured in different ways, but it is recommended that scholars provide a gold standard for different ways to benefit the development of NLP applications. For example, given a text description of an artefact, a dataset may include the novelty scores measured using distance-based and frequency-based approaches while also indicating the reference product databases utilised for the measurement.

Success metrics

A variety of NLP applications have been and will be developed to support various design tasks. To ensure the efficacy of these applications, success metrics are necessary. The metrics like accuracy for classification only tell us that the classifier performs well on the test data. However, the utility of such a classifier is often assessed based on the artefact level metrics such as novelty, quantity and variety. While such metrics are crucial, it would be useful to also measure the ‘goodness’ of envisioned scenarios, activity diagrams, mind maps, opportunity statements, search keywords and requirement formulation.

The expert designers spend a majority of the time proposing and evaluating solution alternatives (Cross Reference Cross2004, p. 430), while novices spend more time understanding the problem. Even if novices generate quick solutions, experts have a better ability to recognise good solutions. Novices could therefore significantly benefit from NLP support in terms of keyword recommendation, opportunity statements, identifying novel solutions and so forth. Since novices need to develop expertise throughout the design process, success metrics at each step could be beneficial for their learning as well as for understanding the efficacy of NLP supports.

4.3. Theoretical directions

While the proposed methodological directions could impact the development of NLP applications in the near future, our review also led us to raise a few questions regarding constructs that embody the design-centric natural language text and the roles of these constructs in the design process. Addressing these questions could be of importance in the extended future to facilitate the development of cognitive assistants that make independent decisions in the design process based on long-term memory and extensive reasoning capabilities. We discuss these questions in the remainder of this section.

Characteristics and constructs

In our review, we have indicated the text characteristics of various types of natural language text sources that are utilised or generated in the design process. These characteristics are only relevant to the NLP methodologies applied to the text sources. The literature does not communicate the characteristics of natural language that allow us to distinguish a piece of text that is relevant to the process. Let us consider the following sentences for example.

  • (i) ‘The pan is heated while the steak gets seasoned,’

  • (ii) ‘During the recrystallization stage, the material is heated above its recrystallization temperature, but below its melting temperature.’

The first sentence mentions a cooking tip and the second one is part of the annealing process.Footnote 56 The underlying factors of distinguishability between these two sentences are unclear. If we assume that the distinction could be attributed to the usage of technical (‘recrystallization’, ‘temperature’, ‘material’) and common-sense (‘pan’ and ‘steak’) terms, it is also possible that these terms could be used interchangeably in other text sources. Hence, we raise the first open question as follows.

What are the unique characteristics of natural language text that are relevant to the design process?

While the efforts to identify the design-specific characteristics in natural language may lead to a bifurcation of technical and common-sense natural language text, it is necessary to acknowledge that design knowledge is present in various flavours within the common-sense text as well. We provide an example using the reviews of a Scotch-Brite kitchen wiper on Amazon.Footnote 57

  • Affordance – ‘I am using this not in kitchen but as a car wash assessary to clean all windows…’

  • Recommendation – ‘You can definitely buy this product…’

  • Satisfaction – ‘The quality of this one is ok’

  • Feature description – ‘…the green color rubber part is very small and thin’

  • Characterisation – ‘I am not sure about this durability’

  • Aesthetics – ‘Too small and badly designed’

  • Technical description – ‘…the actual size of the blade is mere 6.2 inches, which is too small for cleaning a large surface area… the blade is bent at an angle of almost 30–40° to the handle…’

From our review, we are unable to obtain sufficient explanation for the assignment and evolvement of the knowledge categories that we have tied to the sentences in the above example. Scholars have conducted large-scale analyses on consumer opinions while informing a little on what these sources communicate in the context of design. The constructs of design knowledge that embody the natural language text are often captured by ontologies and language models. These systems, however, are not capable of providing a cogent explanation of the phenomenon behind the judgement of design knowledge in a given text. It is therefore important to understand the following.

What are the unique constructs that embody design knowledge into natural language text?

There has been extant literature on ontologies that have aimed to address the question above. These ontologies are built by domain experts (top-down) as well as extracted from text sources (bottom-up). The outcomes of these approaches have often been distinguishable (Panchal and Messer Reference Panchal and Messer2011). In addition, there exists a significant difference in the level of abstraction between elementary (Lee et al. Reference Lee, Kim, Huh, Cho, Park and Lee2013; Varde, Maniruzzaman, and Sisson Reference Varde, Maniruzzaman and Sisson2013) and abstract ontologies (Chandrasekaran and Josephson Reference Chandrasekaran and Josephson2000; Kitamura et al. Reference Kitamura, Sano, Namba and Mizoguchi2002).

Despite the recent attempts to extract abstract ontologies from text, for example, SAPPhIRE (Keshwani and Chakrabarti Reference Keshwani and Chakrabarti2017) and FBS (Fantoni et al. Reference Fantoni, Apreda, Dell’Orletta and Monge2013), it is easier to recognise elementary ontologies, as indicated by various knowledge retrieval systems developed using these. The elementary ontologies, however, do not cover a large scope of design like abstract ontologies. To address the above-mentioned question, it is, therefore, necessary to obtain investigate the following.

How to bridge elementary and abstract ontologies to support the design process?


The following questions relate to the performances of the natural language text concerning comprehension in the design process. Let us consider a natural language explanation for the firing cycle of a Glock handgun.Footnote 58

… when the trigger is pulled, this pulls the firing pin backward … a connector pin that guides the connector in a downward motion… this motion frees up the firing pin, allowing it to strike the primer…

While the above-stated text captures components and the causality of events, it is hard to visualise the orientations and positions of components such as ‘trigger’, ‘firing pin’ and ‘connector pin’ without (annotated-) images. In addition, the text is only pertinent to the firing cycle and does not include other subsystems of the handgun like the safety mechanism. It is difficult to interpret and reproduce the knowledge of system architecture (the hierarchy of a handgun in this example) purely using natural language text. Hence, a multimodal explanation is often necessary, especially in the design process (Siddharth and Chakrabarti Reference Siddharth and Chakrabarti2018). The affordance in comprehension through textual mode shall therefore be investigated as follows.

What is the expected level of comprehension offered by natural language text in the design process?

Addressing the above-stated question could set a boundary for the performance of NLP applications. Large-scale analyses on crowdsourced natural language text (e.g., consumer opinions) often seem to highlight the lack of information quality, while providing less importance to the amount of design knowledge offered within a particular window of text. Since consumer opinions must adhere to word limits on platforms such as Amazon and Twitter, usage scenarios are often captured through images and videos. It would be worth investigating how text could be elaborated such that it provides a level of comprehension similar to that of a multimodal explanation. We, therefore, ask the following question.

How to elaborate natural language text to obtain the desired level of comprehension in the design process?


Cognitive scientists define an insight or ‘Aha’ moment as the instance of sudden realisation that is often associated with a stimulus. In terms of semantic memory, insight occurs when there is a new connection between entities that lead to a sequence of new connections (Schilling Reference Schilling2005). Such insights are necessary for solving problems, especially during the design process. A particular case of insight occurs in the design process when there is a relational alignment between two pairs of entities (Jamrozik and Gentner Reference Jamrozik and Gentner2020).

Let us consider an example. Wall-climbing robots adopt various adhesive mechanisms to establish contact with the climbing surface. These mechanisms are less effective when robots are heavy and the surfaces are hard, flat and smooth. Let us consider a stimulus for this design problem. Mudskippers climb slippery surfaces of rocks by generating a vacuum at the limb interface. The interaction – ‘generate vacuum’ at the rock interface fits well in the wall interface. Such an alignment of relation creates an insight that leads to ‘making sense of new interactions like releasing vacuum and decreasing pressure.

Scholars have proposed representation schemes like FBS and SAPPhIRE to model ‘far’ domain examples such that relations are explicitly shown. A majority of far-domain examples, however, are only available as natural language text that often does not explicitly state these relations. It is difficult to surf through several documents to encounter such relations and experience insights. To address this issue, scholars have proposed to summarise several documents by extracting the representative terms (Luo, Sarica, and Wood Reference Luo, Sarica and Wood2021). These terms alone are insufficient for gathering insights due to the lack of context. It is, therefore, necessary to investigate the following question.

How to represent natural language text such that design insights are maximised?

Souza, Meireles, and Almeida (Reference Souza, Meireles and Almeida2021) generate short summaries of patent documents through an LSTM-based sequence-to-sequence mapping. Such statistical approaches are less guided by design theories that inform the constructs of design knowledge that should be present in such summaries. While a succinct representation of natural language text is necessary for gathering insights, it is also important to form the right queries to search for documents that could potentially include stimuli for solving design problems.

The mental representation of a design problem is translated to opportunity statements that are simplified into search keywords that form queries. It is a common phenomenon that the search results often guide the development of more keywords. If the initial set of keywords is not representative of the problem statement, the user has the chance to be misled by the results. In such situations, an expert could provide reliable guidance on how the problem statement is translated into search keywords by identifying the gaps and discrepancies in the problem formulation.

Let us consider an example of pumping water out of the basement. A direct search for terms like ‘pumping water‘ and ‘basement water’ might lead to several unwanted results. An expert, on the other hand, might question the type of basement, the cause of water in the basement, the type of water and the basement surroundings. These intricate details help elaborate the problem statement, from which the expert could extract important cues and translate these into keywords that are appropriate as well as technical (if necessary). It is therefore worth examining how problems should be narrated such that it is possible to translate these into meaningful opportunity statements and in turn appropriate search keywords.

From our review, we understand that keyword expansion approaches are largely driven by the search results alone (Lin, Chi, and Hsieh Reference Lin, Chi and Hsieh2012; Lee, Mcadams, and Morris Reference Lee, Mcadams and Morris2017) rather than by the missing details of the problem statement. The current NLP applications are therefore less capable of playing the expert’s role in examining the problem statement. To address this caveat, it is necessary that scholars provide a theoretical explanation to the following question.

How to narrate a design problem such that it is better translated to appropriate search keywords?

We expect that in the future, NLP applications recommend keywords that are guided by the problem statement and provide results using succinct natural language text such that more insights are experienced in the design process. Given that insights often lead to solutions to design problems in the form of design concept alternatives, it is necessary to choose among these alternatives for implementation and testing purposes. Several design metrics such as feasibility, novelty and utility are being used to choose the alternatives.

Given that human judgement on alternatives often involves extensive effort and bias, scholars have proposed some NLP applications to compute the design metrics using natural language text data (Gosnell and Miller Reference Gosnell and Miller2015; Siddharth, Madhusudanan, and Chakrabarti Reference Siddharth, Madhusudanan and Chakrabarti2019b). Herein, both the alternatives and reference material (e.g., Kansei attributes) comprise natural language text. Since the usage of terms in the text descriptions of concept alternatives significantly impacts the judgement of design metrics it is important to address the following question.

What is the role of natural language in the judgement of design metrics?

4.4. Summary

From our review of 223 articles related to NLP in-and-for design research, we identified the supported applications in the design process using a framework as discussed in Section 4.1. We have also indicated the steps and modules within the framework that are currently not supported by NLP. While we expect that such gaps are addressed by scholars in the near future, we hope that an NLP guide is developed using a more comprehensive design framework. We expect that such a guide informs the following for an individual module: type of text sources used/generated, example case studies, relevant state-of-the-art NLP methods and rubrics to evaluate NLP methods. After summarising the applications, we presented the directions (listed in Table 9) for the advancement of NLP in-and-for design.

Table 9. Summary of methodological and theoretical directions

The methodological directions are necessary to enhance the performances and conduct a robust evaluation of NLP applications in-and-for design. In Table 9, we have also indicated the downstream tasks and applications that could entail the methodological directions. While design knowledge bases, text generation and named entity recognition could be developed using state-of-the-art NLP approaches, language models and neural machine translation require further improvement in core NLP. For the remaining methodological directions, scholars may consider operationalising the existing design theories into metrics and datasets so that NLP applications could be developed without theoretical challenges.

The theoretical directions call for an understanding of the characteristics and constructs of natural language text that influence the affordance of comprehension and creativity in the design process. As the volume of natural language text data grows multifold with time, it is necessary to distinguish the text that is applicable to the design process. The characteristics and constructs that constitute design language should also indicate the missing elements of design knowledge that influence the abilities to form search keywords, comprehend design text, generate insights and judge the solutions.

The proposed directions primarily call for an understanding of the structure and role of the design language that should help bolster the performances of natural language text in learning, design and computational environments. For example, in a computational environment, a piece of text (e.g., a movie review) may return an accurate sentiment score. In another example, a well-written chapter on kinematics may be useful in a learning environment. These two examples, however, may be less useful in a design environment. Similarly, a design text (e.g., technical requirement) may perform poorly in learning and computational environments. In order not to be misled by the performance in a single environment, it is important to distinguish natural language text by identifying the characteristics and constructs that constitute design language.

5. Conclusions

The purpose of this review article was to encapsulate a large body of NLP contributions that are relevant to the design process so as to identify unsupported design applications, potential methodological advancements and gaps in design theory. We gathered 223 articles published in 32 journals for our review. We organised, explained and examined these articles according to the type of text sources: internal reports, design concepts, discourse transcripts, technical publications and consumer opinions. We then discussed our findings in terms of design applications and future directions. The overall conclusions from the review and the entailing discussions are as follows:

  • (i) A comprehensive NLP guide is necessary for the identification of specific design modules and developing NLP supports according to the type of text sources utilised/generated in these.

  • (ii) While several methodological directions could be pursued using state-of-the-art NLP tools, the development of standard datasets and success metrics require the operationalisation of existing design theories.

  • (iii) It is necessary to identify the unique characteristics and constructs that help distinguish design-centric natural language text as well as influence the performances in terms of comprehension and creativity in the design process.


We use the Web of ScienceFootnote 59 advanced search tool to retrieve the articles for review. We input all queries in the following format,

$$ {\displaystyle \begin{array}{c}\Big(\left(\mathrm{TS}={\mathrm{kw}1}^{\ast}\mathrm{OR}\;{\mathrm{kw}2}^{\ast}\mathrm{OR}\;{\mathrm{kw}3}^{\ast}\dots \right)\;\mathrm{OR}\\ {}\left(\mathrm{TI}={\mathrm{kw}1}^{\ast}\mathrm{OR}\;{\mathrm{kw}2}^{\ast}\mathrm{OR}\;{\mathrm{kw}3}^{\ast}\dots \right)\;\mathrm{OR}\\ {}\left(\mathrm{AB}={\mathrm{kw}1}^{\ast}\mathrm{OR}\;{\mathrm{kw}2}^{\ast}\mathrm{OR}\;{\mathrm{kw}3}^{\ast}\dots \right)\Big)\;\mathrm{AND}\\ {}\left(\mathrm{SO}=\mathrm{dj}1\;\mathrm{OR}\;\mathrm{dj}2\dots \right)\end{array}} $$

where TS = Topic/keyword, TI = Title, AB = Abstract, SO = Journal, kw ∈ {keyword list} and dj $ \in $ {journal list}. We executed the queries on 19th September 2021 and the outcomes of each query are shown in Table A1.

Table A1. Precisions of different queries

We explain the queries as shown in Table A1 for the remainder of this section. In the first query, we consider eight ‘well-known’ design journalsFootnote 60 using the following keywords: ‘semantic’, ‘text’, ‘language’, ‘pars’, ‘ontolog’, ‘abstract’, ‘word’, ‘phras’ and ‘sentence’. We retrieve 890 articles and obtain the frequent terms from topics (> 1), titles (> 4)and abstracts (> 4) to identify more keywords – ‘vocabular’, ‘sentiment’, ‘gramma’, ‘lexic’, ‘linguistic’, ‘syntactic’ and ‘term’. We include these additional keywords in the second query to retrieve 1744 articles. To include more journals that fall within the scope of design research, we consult the literature that provides a broad view of design research (Gemser and de Bont Reference Gemser and de Bont2016; Mansfield Reference Mansfield2016) as well as reviews (Coskun, Zimmerman, and Erbug Reference Coskun, Zimmerman and Erbug2015). Based on the literature, we include five additional journalsFootnote 61 in the third query to retrieve 2328 articles.

Since NLP applications that benefit design research could also be published outside the design journals, in the fourth query we remove the journal filter and retrieve 6,930,765 results. Since these results also include conference proceedings and book chapters, in the fifth query, we select only journal articles to retrieve 4,908,353 articles. To filter these, in the seventh query, we include an additional keyword ‘design’ and particular subject categoriesFootnote 62 to retrieve 78,919 articles. For these articles, we manually selected the journals using the following criteria: article count ≥ 10, nondistant domain (e.g., not ‘Journal of Biological Chemistry’), nonspecific topic (e.g., not ‘Applied Surface Science’), general design-related (e.g., Computers in Industry), technology-related (e.g., Scientometrics). These filters result in 6523 articles.

We merge the results of the third and final queries as the first three queries did not include the ‘design’ keyword filter. We examine the titles and abstractsFootnote 63 of the merged results to obtain 277 articles. Upon reading the full texts of 277 articles, we obtain the final set – 223 articles that we have made accessible on Github.Footnote 64 Using the final set of articles, we also report the precisions of each query as shown in Table A1.


a Denotes the step where we manually read the titles, abstracts and full texts to obtain the final set of articles.

1 Natural Language Toolkit,

5 Bi-directional Encoder Representations from Transformers,

8 By infrastructures, we mean the following: (a) commercial access to high-end computing systems via services like Amazon, Google Cloud etc., (b) open sources Application Programming Interfaces (APIs) provided by several data sources like YouTube, PatentsView etc., (c) the accessibility to open sources codes and algorithms via online communities like HuggingFace.

9 Wherever applicable, we also review the supporting and relevant articles alongside the selected 223 articles.

10 We chose the double diamond model for representing the design process because of its diversity across different streams of design research. If we were to utilise comprehensive, yet specific models, e.g., Pahl and Beitz (Reference Pahl and Beitz2013) that was utilised by Chiarello, Belingheriand Fantoni (Reference Chiarello, Belingheri and Fantoni2021), it is difficult to choose between embodiment and detailed design phases for categorising articles.

11 Design rationale that is mentioned here refers to the previously recorded decisions and underlying reasons that are pertinent to past design issues.

16 Bugs reported on Chrome web browser from the Mining Software Repositories Mining Challenge,

17 SAPPhIRE is a model of causality that comprises the following constructs: States, Actions, Parts, Phenomena, Inputs, oRgans and Effects.

20 Taught at the University of California, Berkeley.

30 The selected patents fall within the A47G33, A61G1-G13 groups that are defined by the International Patent Classification (IPC).

43 SHapley Additive Explanations -

48 International Classification of Functioning, Disability, and Health,

52 Commercial Buildings Energy Consumption Survey -

New York City Local Law 84 (LL84) -

New York City Primary Land Use Tax Lot Output (PLUTO) -


60 We included the following design journals: Artificial Intelligence for Engineering Design Analysis and Manufacturing, Research in Engineering Design, Journal of Engineering Design, Design Studies, Design Science, Journal of Mechanical Design, Journal of Computing and Information Science in Engineering, International Journal of Design.

61 We include the following journals in addition to the previously populated list of design journals: Design Journal, Design Quarterly, Design Issues, International Journal of Design Creativity and Innovation, Journal of Computer-Aided Design.

62 The selected categories include the following: Engineering Manufacturing, Engineering Multidisciplinary, Engineering Mechanical, Computer Science Interdisciplinary Applications, Computer Science Software Engineering, Art, Computer Science Artificial Intelligence, Engineering Industrial, Social Sciences Interdisciplinary, Architecture.

63 An example of discarded result had the following text within the abstract: ‘… material properties that are computed in terms of the microstructural texture descriptors.’ (Acar Reference Acar2020).


Acar, P. 2020 A new sampling approach for the multi-scale design of metallic materials. Journal of Mechanical Design 142, 081702; doi:10.1115/1.4045800.CrossRefGoogle Scholar
Agyemang, M., Linsey, J. & Turner, C. J. 2017 Transforming functional models to critical chain models via expert knowledge and automatic parsing rules for design analogy identification. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 31, 501511; doi:10.1017/S0890060417000488.Google Scholar
Ahmed, F. & Fuge, M. 2018 Creative exploration using topic-based bisociative networks. Design Science 4, e12; doi:10.1017/dsj.2018.5.CrossRefGoogle Scholar
Akmal, S., Shih, L.-H. & Batres, R. 2014 Ontology-based similarity for product information retrieval. Computers in Industry 65, 91107; doi:10.1016/j.compind.2013.07.011.CrossRefGoogle Scholar
Al-Natsheh, H.T., Martinet, L., Muhlenbach, F., Zighed, D.A. 2017. UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features, in: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp. 115–119. Scholar
Altuntas, S., Erdogan, Z. & Dereli, T. 2020 A clustering-based approach for the evaluation of candidate emerging technologies. Scientometrics 124, 11571177; doi:10.1007/s11192-020-03535-0.CrossRefGoogle Scholar
Ameri, F., Kulvatunyou, B., Ivezic, N. & Kaikhah, K. 2014 Ontological conceptualization based on the SKOS. Journal of Computing and Information Science in Engineering 14, 031006; doi:10.1115/1.4027582.CrossRefGoogle Scholar
Andersen, A. & Munch, K. 1991 Automatic-generation of technical documentation. Expert Systems with Applications 3, 219227; doi:10.1016/0957-4174(91)90150-D.CrossRefGoogle Scholar
Argente, D., Baslandze, S., Hanley, D., Moreira, S., 2020. Patents to Products: Product Innovation and Firm Dynamics (FRB Atlanta Working Paper No. 2020–4). Federal Reserve Bank of Atlanta. Scholar
Arnarsson, I. O., Frost, O., Gustavsson, E., Jirstrand, M. & Malmqvist, J. 2021 Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents. Concurrent Engineering: Research and Applications 29, 142152; doi:10.1177/1063293X20982973.CrossRefGoogle Scholar
Arts, S., Hou, J. & Gomez, J. C. 2021 Natural language processing to identify the creation and impact of new technologies in patent text: code, data, and new measures. Research Policy 50, 104144; doi:10.1016/j.respol.2020.104144.CrossRefGoogle Scholar
Atherton, M., Jiang, P., Harrison, D. & Malizia, A. 2018 Design for invention: annotation of functional geometry interaction for representing novel working principles. Research in Engineering Design 29, 245262; doi:10.1007/s00163-017-0267-2.CrossRefGoogle ScholarPubMed
Aurisicchio, M., Bracewell, R. & Hooey, B. L. 2016 Rationale mapping and functional modelling enhanced root cause analysis. Safety Science 85, 241257.CrossRefGoogle Scholar
Baccianella, S., Esuli, A. & Sebastiani, F. 2010 SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA).Google Scholar
Ball, Z. & Lewis, K. 2020 Predicting design performance utilizing automated topic discovery. Journal of Mechanical Design 142, 121703; doi:10.1115/1.4048455.CrossRefGoogle Scholar
Bengio, Y. 2009 Learning deep architectures for AI. Foundations and Trends® in Machine Learning 2, 1127; doi:10.1561/2200000006.CrossRefGoogle Scholar
Biswas, A., Fenves, S. J., Shapiro, V. & Sriram, R. 2008 Representation of heterogeneous material properties in the core product model. Engineering with Computers 24, 4358; doi:10.1007/s00366-007-0065-y.CrossRefGoogle Scholar
Bohm, M. R., Stone, R. B. & Szykman, S. 2005 Enhancing virtual product representations for advanced design repository systems. Journal of Computing and Information Science in Engineering 5, 360372.CrossRefGoogle Scholar
Botta, D. & Woodbury, R. 2013 Predicting topic shift locations in design histories. Research in Engineering Design 24, 245258; doi:10.1007/s00163-012-0141-1.CrossRefGoogle Scholar
Bouhana, A., Zidi, A., Fekih, A., Chabchoub, H. & Abed, M. 2015 An ontology-based CBR approach for personalized itinerary search systems for sustainable urban freight transport. Expert Systems with Applications 42, 37243741; doi:10.1016/j.eswa.2014.12.012.CrossRefGoogle Scholar
Bradley, M., Lang, P., 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings, in: Technical Report C-1. The Center for Research in Psychophysiology, University of Florida.Google Scholar
Brisco, R., Whitfield, R. I. & Grierson, H. 2020 A novel systematic method to evaluate computer-supported collaborative design technologies. Research in Engineering Design 31, 5381; doi:10.1007/s00163-019-00323-7.CrossRefGoogle Scholar
Cambria, E., Li, Y., Xing, F. Z., Poria, S. & Kwok, K. 2020 SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, CIKM‘20, pp. 105114. Association for Computing Machinery; doi:10.1145/3340531.3412003.Google Scholar
Camburn, B., Arlitt, R., Anderson, D., Sanaei, R., Raviselam, S., Jensen, D. & Wood, K. L. 2020a Computer-aided mind map generation via crowdsourcing and machine learning. Research in Engineering Design 31, 383409; doi:10.1007/s00163-020-00341-w.CrossRefGoogle Scholar
Camburn, B., He, Y., Raviselvam, S., Luo, J. & Wood, K. 2020b Machine learning-based design concept evaluation. Journal of Mechanical Design 142, 031113; doi:10.1115/1.4045126.CrossRefGoogle Scholar
Cao, G., Sun, Y., Tan, R., Zhang, J. & Liu, W. 2021 A function-oriented biologically analogical approach for constructing the design concept of smart product in industry 4.0. Advanced Engineering Informatics 49, 101352; doi:10.1016/j.aei.2021.101352.CrossRefGoogle Scholar
Casakin, H. & Georgiev, G. V. 2021 Design creativity and the semantic analysis of conversations in the design studio. International Journal of Design Creativity and Innovation 9, 6177; doi:10.1080/21650349.2020.1838331.CrossRefGoogle Scholar
Casamayor, A., Godoy, D. & Campo, M. 2012 Functional grouping of natural language requirements for assistance in architectural software design. Knowledge-Based Systems 30, 7886; doi:10.1016/j.knosys.2011.12.009.CrossRefGoogle Scholar
Cascini, G. & Rissone, P. 2004 Plastics design: integrating TRIZ creativity and semantic knowledge portals. Journal of Engineering Design 15, 405424; doi:10.1080/09544820410001697208.CrossRefGoogle Scholar
Cascini, G. & Zini, M. 2008 Measuring patent similarity by comparing inventions functional trees. In Computer-Aided Innovation (CAI) (ed. Cascini, G.), pp. 3142. Springer US.CrossRefGoogle Scholar
Cavazza, M. & Zweigenbaum, P. 1994 Semantic analysis and in-depth understanding of technical texts. Applied Artificial Inteligence 8, 425453; doi:10.1080/08839519408945452.CrossRefGoogle Scholar
Chaklader, R. & Parkinson, M. B. 2017 Data-driven sizing specification utilizing consumer text reviews. Journal of Mechanical Design 139, 111406; doi:10.1115/1.4037476.CrossRefGoogle Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B. & Nataraju, B. 2005 A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, 113132; doi:10.1017/S0890060405050109.CrossRefGoogle Scholar
Chandrasekaran, B. 2005 Representing function: relating functional representation and functional modeling research streams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, 6574; doi:10.1017/S0890060405050079.CrossRefGoogle Scholar
Chandrasekaran, B. & Josephson, J. R. 2000 Function in device representation. Engineering with Computers 16, 162177; doi:10.1007/s003660070003.CrossRefGoogle Scholar
Chang, D. & Chen, C.-H. 2015 Product concept evaluation and selection using data mining and domain ontology in a crowdsourcing environment. Advanced Engineering Informatics 29, 759774; doi:10.1016/j.aei.2015.06.003.CrossRefGoogle Scholar
Chang, D. & Lee, C. 2018 A product affective properties identification approach based on web mining in a crowdsourcing environment. Journal of Engineering Design 29, 449483; doi:10.1080/09544828.2018.1463514.CrossRefGoogle Scholar
Chanyachatchawan, S., Yan, H.-B., Sriboonchitta, S. & Huynh, V.-N. 2017 A linguistic representation based approach to modelling Kansei data and its application to consumer-oriented evaluation of traditional products. Knowledge-Based Systems 138, 124133; doi:10.1016/j.knosys.2017.09.037.CrossRefGoogle Scholar
Chen, C., Tao, Y., Li, Y., Liu, Q., Li, S. & Tang, Z. 2021 A structure-function knowledge extraction method for bio-inspired design. Computers in Industry 127, 103402; doi:10.1016/j.compind.2021.103402.CrossRefGoogle Scholar
Chen, R., Chen, C.-H., Liu, Y. & Ye, X. 2020 Ontology-based requirement verification for complex systems. Advanced Engineering Informatics 46, 101148; doi:10.1016/j.aei.2020.101148.CrossRefGoogle Scholar
Chen, T.-J. & Krishnamurthy, V. R. 2020 Investigating a mixed-initiative workflow for digital mind-mapping. Journal of Mechanical Design 142, 101404; doi:10.1115/1.4046808.CrossRefGoogle Scholar
Chen, Y., Yu, S., Chu, J., Chen, D. & Yu, M. 2021 Evaluating aircraft cockpit emotion through a neural network approach. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 35, 8198; doi:10.1017/S0890060420000475.Google Scholar
Cheong, H., Chiu, I., Shu, L., Stone, R. & McAdams, D. 2011 Biologically meaningful keywords for functional terms of the functional basis. Journal of Mechanical Design 133, 021007.CrossRefGoogle Scholar
Cheong, H., Li, W., Cheung, A., Nogueira, A. & Iorio, F. 2017 Automated extraction of function knowledge from text. Journal of Mechanical Design 139, 111407; doi:10.1115/1.4037817.CrossRefGoogle Scholar
Cheong, H. & Shu, L. H. 2014 Retrieving causally related functions from natural-language text for biomimetic design. Journal of Mechanical Design 136, 081008; doi:10.1115/1.4027494.CrossRefGoogle Scholar
Chiarello, F., Belingheri, P. & Fantoni, G. 2021 Data science for engineering design: state of the art and future directions. Computers in Industry 129, 103447; doi:10.1016/j.compind.2021.103447.CrossRefGoogle Scholar
Chiarello, F., Bonaccorsi, A. & Fantoni, G. 2020 Technical sentiment analysis. Measuring advantages and drawbacks of new products using social media. Computers in Industry 123, 103299. Scholar
Chiarello, F., Cimino, A., Fantoni, G. & Dell’Orletta, F. 2018 Automatic users extraction from patents. World Patent Information 54, 2838; doi:10.1016/j.wpi.2018.07.006.CrossRefGoogle Scholar
Chiarello, F., Fantoni, G. & Bonaccorsi, A. 2017 Product description in terms of advantages and drawbacks: Exploiting patent information in novel ways. Presented at the DS 87–6 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 6: Design Information and Knowledge, Vancouver, Canada, 21–25.08. 2017, pp. 101110.Google Scholar
Chiu, I. & Shu, L. 2007 Using language as related stimuli for concept generation. AI EDAM 21, 103121.Google Scholar
Chiu, I. & Shu, L. H. 2012 Investigating effects of oppositely related semantic stimuli on design concept creativity. Journal of Engineering Design 23, 271296; doi:10.1080/09544828.2011.603298.CrossRefGoogle Scholar
Chiu, M.-C. & Lin, K.-Z. 2018 Utilizing text mining and Kansei engineering to support data-driven design automation at conceptual design stage. Advanced Engineering Informatics 38, 826839; doi:10.1016/j.aei.2018.11.002.CrossRefGoogle Scholar
Choi, K. & Jun, C. 2007 A systematic approach to the Kansei factors of tactile sense regarding the surface roughness. Applied Ergonomics 38, 5363; doi:10.1016/j.apergo.2006.01.003.CrossRefGoogle Scholar
Chomsky, N. 2014 Aspects of the Theory of Syntax. MIT Press Limited.Google Scholar
Chou, J.-R. 2014 An ideation method for generating new product ideas using TRIZ, concept mapping, and fuzzy linguistic evaluation techniques. Advanced Engineering Informatics 28, 441454; doi:10.1016/j.aei.2014.06.006.CrossRefGoogle Scholar
Chou, J.-R. 2016 A Kansei evaluation approach based on the technique of computing with words. Advanced Engineering Informatics 30, 115; doi:10.1016/j.aei.2015.11.001.CrossRefGoogle Scholar
Coskun, A., Zimmerman, J. & Erbug, C. 2015 Promoting sustainability through behavior change: a review. Design Studies 41, 183204; doi:10.1016/j.destud.2015.08.008.CrossRefGoogle Scholar
Cross, N. 2004 Expertise in design: an overview. Design Studies 25, 427441; doi:10.1016/j.destud.2004.06.002.CrossRefGoogle Scholar
De Vries, B., Jessurun, J., Segers, N. & Achten, H. 2005 Word graphs in architectural design. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 19, 277288; doi:10.1017/S0890060405050195.Google Scholar
Decker, R. & Trusov, M. 2010 Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing 27, 293307; doi:10.1016/j.ijresmar.2010.09.001.CrossRefGoogle Scholar
Deken, F., Kleinsmann, M., Aurisicchio, M., Lauche, K. & Bracewell, R. 2012 Tapping into past design experiences: knowledge sharing and creation during novice-expert design consultations. Research in Engineering Design 23, 203218; doi:10.1007/s00163-011-0123-8.CrossRefGoogle Scholar
Delin, J., Sharoff, S., Lillford, S. & Barnes, C. 2007 Linguistic support for concept selection decisions. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 21, 123135; doi:10.1017/S0890060407070187.Google Scholar
Desmet, P. M. A. 2012 Faces of product pleasure: 25 positive emotions in human-product interactions. International Journal of Design 6, 129.Google Scholar
Ding, L., Davies, D., McMahon, C. A. 2009 The integration of lightweight representation and annotation for collaborative design representation. Research In Engineering Design 19, 223238; doi:10.1007/s00163-008-0052-3.CrossRefGoogle Scholar
Dirnberger, D. 2016 The use of mindmapping software for patent search and management. World Patent Information 47, 1220; doi:10.1016/j.wpi.2016.08.004.CrossRefGoogle Scholar
Dong, A. 2005 The latent semantic approach to studying design team communication. Design Studies 26, 445461.CrossRefGoogle Scholar
Dong, A. 2006 Concept formation as knowledge accumulation: a computational linguistics study. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 20, 3553; doi:10.1017/S0890060406060033.Google Scholar
Dong, A. 2007 The enactment of design through language. Design Studies 28, 521; doi:10.1016/j.destud.2006.07.001.CrossRefGoogle Scholar
Dong, A., Hill, A. W. & Agogino, A. M. 2004 A document analysis method for characterizing design team performance. Journal of Mechanical Design 126, 378385.CrossRefGoogle Scholar
Dong, A., Sarkar, S., Yang, M. C. & Honda, T. 2014 A linguistic approach to assess the dynamics of design team preference in concept selection. Research in Engineering Design 25, 7592.CrossRefGoogle Scholar
Dong, Y., Zhu, R., Peng, W., Tian, Q., Guo, G. & Liu, W. 2021 A fuzzy mapping method for Kansei needs interpretation considering the individual Kansei variance. Research in Engineering Design 32, 175187; doi:10.1007/s00163-021-00359-8.CrossRefGoogle Scholar
Ebrahimipour, V., Rezaie, K. & Shokravi, S. 2010 An ontology approach to support FMEA studies. Expert Systems with Applications 37, 671677; doi:10.1016/j.eswa.2009.06.033.CrossRefGoogle Scholar
El Dehaibi, N., Goodman, N. D. & MacDonald, E. F. 2019 Extracting customer perceptions of product sustainability from online reviews. Journal of Mechanical Design 141, 121103; doi:10.1115/1.4044522.CrossRefGoogle Scholar
Eng, N., Aurisicchio, M. & Bracewell, R. 2017 Mapping software augments engineering design thinking. Journal of Mechanical Design 139, 051103.CrossRefGoogle Scholar
Fantoni, G., Apreda, R., Dell’Orletta, F. & Monge, M. 2013 Automatic extraction of function–behaviour–state information from patents. Advanced Engineering Informatics 27, 317334; doi:10.1016/j.aei.2013.04.004.CrossRefGoogle Scholar
Fantoni, G., Coli, E., Chiarello, F., Apreda, R., Dell’Orletta, F., & Pratelli, G. 2021 Text mining tool for translating terms of contract into technical specifications: development and application in the railway sector. Computers in Industry 124, 103357; doi:10.1016/j.compind.2020.103357.CrossRefGoogle Scholar
Farley, B. 2001 Extracting information from free-text aircraft repair notes. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 15, 295305; doi:10.1017/S0890060401154041.Google Scholar
Fenves, S. J., Foufou, S., Bock, C. & Sriram, R. D. 2008 CPM2: a core model for product data. Journal of Computing and Information Science in Engineering 8, 014501; doi:10.1115/1.2830842.CrossRefGoogle Scholar
Fu, K., Cagan, J., Kotovsky, K. & Wood, K. 2013a Discovering structure in design databases through functional and surface based mapping. Journal of Mechanical Design 135, 031006; doi:10.1115/1.4023484.CrossRefGoogle Scholar
Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C. & Wood, K. 2013b The meaning of “near” and “far”: the impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design 135, 021007.CrossRefGoogle Scholar
Fuge, M., Peters, B. & Agogino, A. 2014 Machine learning algorithms for recommending design methods. Journal of Mechanical Design 136, 101103; doi:10.1115/1.4028102.CrossRefGoogle Scholar
Gangopadhyay, A. 2001 Conceptual modeling from natural language functional specifications. Artificial Inteligence in Engineering 15, 207218; doi:10.1016/S0954-1810(01)00017-6.CrossRefGoogle Scholar
Garcia, A. C. B., Ferraz, I. & Vivacqua, A. S. 2009 From data to knowledge mining. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 23, 427441; doi:10.1017/S089006040900016X.Google Scholar
Gemser, G. & de Bont, C. 2016 Design-related and design-focused research: a study of publication patterns in design journals. She Ji: The Journal of Design, Economics, and Innovation 2, 4658; doi:10.1016/j.sheji.2016.05.002.Google Scholar
Georgiev, G. V. & Georgiev, D. D. 2018 Enhancing user creativity: semantic measures for idea generation. Knowledge-Based Systems 151, 115; doi:10.1016/j.knosys.2018.03.016.CrossRefGoogle Scholar
Gericke, K. & Eisenbart, B. 2017 The integrated function modeling framework and its relation to function structures. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 436457; doi:10.1017/S089006041700049X.Google Scholar
Gero, J. S. & Kannengiesser, U. 2012 Representational affordances in design, with examples from analogy making and optimization. Research in Engineering Design 23, 235249; doi:10.1007/s00163-012-0128-y.CrossRefGoogle Scholar
Giordano, V., Chiarello, F., Cervelli, E., 2021. Defining definition: a Text mining Approach to Define Innovative Technological Fields. arXiv preprints Information Retrieval (cs.IR). Scholar
Glier, M. W., McAdams, D. A. & Linsey, J. S. 2014 Exploring automated text classification to improve keyword corpus search results for bioinspired design. Journal of Mechanical Design 136, 111103; doi:10.1115/1.4028167.CrossRefGoogle Scholar
Glock, F. 2009 Aspects of language use in design conversation. Codesign-International Journal of Cocreation in Design and The Arts 5, 519; doi:10.1080/15710880802492870.Google Scholar
Goel, A. K. & Bhatta, S. R. 2004 Use of design patterns in analogy-based design. Advanced Engineering Informatics 18, 8594.CrossRefGoogle Scholar
Goel, A. K., Vattam, S., Wiltgen, B. & Helms, M. 2012. Cognitive, collaborative, conceptual and creative—Four characteristics of the next generation of knowledge-based CAD systems: a study in biologically inspired design. Computer-Aided Design 44, 879900.CrossRefGoogle Scholar
Goepp, V., Matta, N., Caillaud, E. & Feugeas, F. 2019 Systematic community of practice activities evaluation through natural language processing: application to research projects. AI EDAM: Artificial Inteligence for Engineering Design, Analysis and Manufacturing 33, 160171; doi:10.1017/S0890060419000076.Google Scholar
Gosnell, C. A. & Miller, S. R. 2015 But is it creative? Delineating the impact of expertise and concept ratings on creative concept selection. Journal of Mechanical Design 138, 021101; doi:10.1115/1.4031904.CrossRefGoogle Scholar
Goucher-Lambert, K. & Cagan, J. 2019 Crowdsourcing inspiration: using crowd generated inspirational stimuli to support designer ideation. Design Studies 61, 129; doi:10.1016/j.destud.2019.01.001.CrossRefGoogle Scholar
Goucher-Lambert, K., Gyory, J. T., Kotovsky, K. & Cagan, J. 2020 Adaptive inspirational design stimuli: using design output to computationally search for stimuli that impact concept generation. Journal of Mechanical Design 142, 091401; doi:10.1115/1.4046077.CrossRefGoogle Scholar
Gozuacik, N., Sakar, C. O. & Ozcan, S. 2021 Social media-based opinion retrieval for product analysis using multi-task deep neural networks. Expert Systems with Applications 183, 115388; doi:10.1016/j.eswa.2021.115388.CrossRefGoogle Scholar
Gu, N., Xu, J., Wu, X., Yang, J. & Ye, W. 2005 Ontology based semantic conflicts resolution in collaborative editing of design documents. Advanced Engineering Informatics 19, 103111; doi:10.1016/j.aei.2005.05.005.CrossRefGoogle Scholar
Guo, F., Hu, M., Duffy, V. G., Shao, H. & Ren, Z. 2021 Kansei evaluation for group of users: a data-driven approach using dominance-based rough sets. Advanced Engineering Informatics 47, 101241; doi:10.1016/j.aei.2020.101241.CrossRefGoogle Scholar
Guo, Y., Peng, Y. & Hu, J. 2013 Research on high creative application of case-based reasoning system on engineering design. Computers in Industry 64, 90103; doi:10.1016/j.compind.2012.10.006.CrossRefGoogle Scholar
Han, J., Sarica, S., Shi, F. & Luo, J. 2021 Semantic networks for engineering design: state of the art and future directions. Journal of Mechanical Design 144, 020802; doi:10.1115/1.4052148.Google Scholar
Han, J., Shi, F., Chen, L. & Childs, P. R. N. 2018a A computational tool for creative idea generation based on analogical reasoning and ontology. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 462477; doi:10.1017/S0890060418000082.Google Scholar
Han, J., Shi, F., Chen, L. & Childs, P. R. N. 2018b The combinator—A computer-based tool for creative idea generation based on a simulation approach. Design Science 4, e11; doi:10.1017/dsj.2018.7.CrossRefGoogle Scholar
Han, Y. & Moghaddam, M. 2021a Eliciting attribute-level user needs from online reviews with deep language models and information extraction. Journal of Mechanical Design 143, 061403; doi:10.1115/1.4048819.CrossRefGoogle Scholar
Han, Y. & Moghaddam, M. 2021b Analysis of sentiment expressions for user-centered design. Expert Systems with Applications 171, 114604; doi:10.1016/j.eswa.2021.114604.CrossRefGoogle Scholar
Hao, J., Zhao, Q. & Yan, Y. 2017 A function-based computational method for design concept evaluation. Advanced Engineering Informatics 32, 237247; doi:10.1016/j.aei.2017.03.002.CrossRefGoogle Scholar
Hatchuel, A., Weil, B. & Le Masson, P. 2013 Towards an ontology of design: lessons from C-K design theory and Forcing. Research in Engineering Design 24, 147163; doi:10.1007/s00163-012-0144-y.CrossRefGoogle Scholar
He, R., Lee, W. S., Ng, H. T. & Dahlmeier, D. 2018 Exploiting document knowledge for aspect-level sentiment classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 579585. Association for Computational Linguistics; doi:10.18653/v1/P18-2092.CrossRefGoogle Scholar
He, Y., Camburn, B., Liu, H., Luo, J., Yang, M. & Wood, K. L. 2019 Mining and representing the concept space of existing ideas for directed ideation. Journal of Mechanical Design 141, 121101; doi:10.1115/1.4044399.CrossRefGoogle Scholar
Hiekata, K., Yamato, H. & Tsujimoto, S. 2010 Ontology based knowledge extraction for shipyard fabrication workshop reports. Expert Systems with Applications 37, 73807386; doi:10.1016/j.eswa.2010.04.031.CrossRefGoogle Scholar
Hou, T., Yannou, B., Leroy, Y. & Poirson, E. 2019 Mining changes in user expectation over time from online reviews. Journal of Mechanical Design 141, 091102; doi:10.1115/1.4042793.CrossRefGoogle Scholar
Hsiao, C., Ruffino, M., Malak, R., Tumer, I. Y. & Doolen, T. 2016 Discovering taxonomic structure in design archives with application to risk-mitigating actions in a large engineering organisation. Journal of Engineering Design 27, 146169; doi:10.1080/09544828.2015.1126813.CrossRefGoogle Scholar
Hsieh, S.-H., Lin, H.-T., Chi, N.-W., Chou, K.-W. & Lin, K.-Y. 2011 Enabling the development of base domain ontology through extraction of knowledge from engineering domain handbooks. Advanced Engineering Informatics 25, 288296; doi:10.1016/j.aei.2010.08.004.CrossRefGoogle Scholar
Hsieh, W.-T., Stu, J., Chen, Y.-L. & Chou, S.-C. T. 2009 A collaborative desktop tagging system for group knowledge management based on concept space. Expert Systems with Applications 36, 95139523; doi:10.1016/j.eswa.2008.12.042.CrossRefGoogle Scholar
Hu, M., Guo, F., Duffy, V. G., Ren, Z. & Yue, P. 2020 Constructing and measuring domain-specific emotions for affective design: a descriptive approach to deal with individual differences. Ergonomics 63, 563578; doi:10.1080/00140139.2020.1735528.CrossRefGoogle ScholarPubMed
Hubka, V. & Eder, W. E. 1990 Design knowledge: theory in support of practice. Journal of Engineering Design 1, 97108; doi:10.1080/09544829008901646.CrossRefGoogle Scholar
Huet, A., Pinquie, R., Veron, P., Mallet, A. & Segonds, F. 2021 CACDA: a knowledge graph for a context-aware cognitive design assistant. Computers in Industry 125, 103377; doi:10.1016/j.compind.2020.103377.CrossRefGoogle Scholar
Jamrozik, A. & Gentner, D. 2020 Relational labeling unlocks inert knowledge. Cognition 196, 104146; doi:10.1016/j.cognition.2019.104146.CrossRefGoogle ScholarPubMed
Jang, H., Jeong, Y. & Yoon, B. 2021 TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042; doi:10.1016/j.eswa.2020.114042.CrossRefGoogle Scholar
Jeon, S. M., Lee, J. H., Hahm, G. J. & Suh, H. W. 2016 Automatic CAD model retrieval based on design documents using semantic processing and rule processing. Computers in Industry 77, 2947; doi:10.1016/j.compind.2016.01.002.CrossRefGoogle Scholar
Ji, H., Yang, M. C. & Honda, T. 2012 An approach to the extraction of preference-related information from design team language. Research in Engineering Design 23, 85103; doi:10.1007/s00163-011-0116-7.CrossRefGoogle Scholar
Jiang, H., Kwong, C. K., Kremer, G. E. O. & Park, W.-Y. 2019 Dynamic modelling of customer preferences for product design using DENFIS and opinion mining. Advanced Engineering Informatics 42, 100969; doi:10.1016/j.aei.2019.100969.CrossRefGoogle Scholar
Jiang, H., Kwong, C. K., Park, W. Y. & Yu, K. M. 2018 A multi-objective PSO approach of mining association rules for affective design based on online customer reviews. Journal of Engineering Design 29, 381403; doi:10.1080/09544828.2018.1475629.CrossRefGoogle Scholar
Jiang, H., Kwong, C. K. & Yung, K. L. 2017. Predicting future importance of product features based on online customer reviews. Journal of Mechanical Design 139, 111413, doi:10.1115/1.4037348.CrossRefGoogle Scholar
Jiang, S., Hu, J., Magee, C.L., Luo, J., 2022. Deep Learning for Technical Document Classification. IEEE Transactions on Engineering Management Early Access, 117. Scholar
Jin, G., Jeong, Y. & Yoon, B. 2015 Technology-driven roadmaps for identifying new product/market opportunities: use of text mining and quality function deployment. Advanced Engineering Informatics 29, 126138; doi:10.1016/j.aei.2014.11.001.CrossRefGoogle Scholar
Jin, J., Ji, P. & Gu, R. 2016 Identifying comparative customer requirements from product online reviews for competitor analysis. Engineering Applications of Artificial Intelligence 49, 6173; doi:10.1016/j.engappai.2015.12.005.CrossRefGoogle Scholar
Jin, J., Ji, P. & Liu, Y. 2014 Prioritising engineering characteristics based on customer online reviews for quality function deployment. Journal of Engineering Design 25, 303324; doi:10.1080/09544828.2014.984665.CrossRefGoogle Scholar
Jin, J., Ji, P. & Liu, Y. 2015 Translating online customer opinions into engineering characteristics in QFD: a probabilistic language analysis approach. Engineering Applications of Artificial Inteligence 41, 115127; doi:10.1016/j.engappai.2015.02.006.CrossRefGoogle Scholar
Jin, J., Liu, Y., Ji, P. & Kwong, C. K. 2019 Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering 19, 010801; doi:10.1115/1.4041087.CrossRefGoogle Scholar
Jo, Y. & Oh, A. H. 2011 Aspect and sentiment unification model for online review analysis. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ‘11, pp. 815824. Association for Computing Machinery; doi:10.1145/1935826.1935932.CrossRefGoogle Scholar
Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. 2017 Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427431. Association for Computational Linguistics.Google Scholar
Joung, J. & Kim, H. M. 2021 Approach for importance-performance analysis of product attributes from online reviews. Journal of Mechanical Design 143, 081705; doi:10.1115/1.4049865.CrossRefGoogle Scholar
Kan, J. W. T. & Gero, J. S. 2018 Characterizing innovative processes in design spaces through measuring the information entropy of empirical data from protocol studies. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 32, 3243; doi:10.1017/S0890060416000548.Google Scholar
Kang, S., Path, L., Rangarajan, A., Moitra, A., Jia, T., Robinson, D. & Dutta, D. 2019a Automated feedback generation for formal manufacturing rule extraction. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 33, 289301; doi:10.1017/S0890060419000027.Google Scholar
Kang, S., Patil, L., Rangarajan, A., Moitra, A., Robinson, D., Jia, T. & Dutta, D. 2019b Ontology-based ambiguity resolution of manufacturing text for formal rule extraction. Journal of Computing and Information Science in Engineering 19, 021003; doi:10.1115/1.4042104.CrossRefGoogle Scholar
Kang, S. W. & Tucker, C. 2016 An automated approach to quantifying functional interactions by mining large-scale product specification data. Journal of Engineering Design 27, 124; doi:10.1080/09544828.2015.1083539.CrossRefGoogle Scholar
Kano, N. 1984 Attractive quality and must-be quality. Hinshitsu (Quality, The Journal of Japanese Society for Quality Control) 14, 3948.Google Scholar
Karlsson, B., Aronsson, N. & Svensson, K. 2003 Using semantic environment description as a tool to evaluate car interiors. Ergonomics 46, 14081422; doi:10.1080/00140130310001624905.CrossRefGoogle ScholarPubMed
Keshwani, S. & Chakrabarti, A. 2017 Towards automatic classification of description of analogies into SAPPhIRE constructs. In: International Conference on Research into Design, pp. 643655. Springer.Google Scholar
Kestel, P., Kuegler, P., Zirngibl, C., Schleich, B. & Wartzack, S. 2019 Ontology-based approach for the provision of simulation knowledge acquired by data and text mining processes. Advanced Engineering Informatics 39, 292305; doi:10.1016/j.aei.2019.02.001.CrossRefGoogle Scholar
Kim, S., Bracewell, R. H. & Wallace, K. M. 2007 Answering engineers’ questions using semantic annotations. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 21, 155171; doi:10.1017/S0890060407070205.Google Scholar
Kim, S.-J. & Lee, J.-H. 2017 A study on metadata structure and recommenders of biological systems to support bio-inspired design. Engineering Applications of Artificial Inteligence 57, 1641; doi:10.1016/j.engappai.2016.10.003.CrossRefGoogle Scholar
Kim, W., Ko, T., Rhiu, I. & Yun, M. H. 2019 Mining affective experience for a Kansei design study on a recliner. Applied Ergonomics 74, 145153; doi:10.1016/j.apergo.2018.08.014.CrossRefGoogle Scholar
Kitamura, Y., Sano, T., Namba, K. & Mizoguchi, R. 2002 A functional concept ontology and its application to automatic identification of functional structures. Advanced Engineering Informatics 16, 145163; doi:10.1016/S1474-0346(02)00006-X.CrossRefGoogle Scholar
Ko, W.-C. 2015 Construction of house of quality for new product planning: A 2-tuple fuzzy linguistic approach. Computers in Industry 73, 117127; doi:10.1016/j.compind.2015.07.008.CrossRefGoogle Scholar
Korobkin, D., Fomenkov, S., Kravets, A., Kolesnikov, S. & Dykov, M. 2015 Three-steps methodology for patents prior-art retrieval and structured physical knowledge extracting. Communications in Computer and Information Science 535, 124136.Google Scholar
Korpershoek, H., Kuyper, H., van der Werf, G. & Bosker, R. 2010 Who ‘fits’ the science and technology profile? Personality differences in secondary education. Journal of Research in Personality 44, 649654; doi:10.1016/j.jrp.2010.08.010.CrossRefGoogle Scholar
Kott, A. & Peasant, J. 1995 Representation and management of requirements: the RAPID-WS project. Concurrent Engineering-Research and Applications 3, 93106; doi:10.1177/1063293X9500300203.CrossRefGoogle Scholar
Kurtanović, Z. & Maalej, W. 2018 On user rationale in software engineering. Requirements Engineering 23, 357379; doi:10.1007/s00766-018-0293-2.CrossRefGoogle Scholar
Lan, L., Liu, Y. & Feng Lu, W. 2017 Automatic discovery of design task structure using deep belief nets. Journal of Computing and Information Science in Engineering 17, 041001; doi:10.1115/1.4036198.CrossRefGoogle Scholar
Lan, L., Liu, Y. & Lu, W. F. 2018 Learning from the past: uncovering design process models using an enriched process mining. Journal of Mechanical Design 140, 041403; doi:10.1115/1.4039200.CrossRefGoogle Scholar
Lee, C., Jeon, D., Ahn, J. M. & Kwon, O. 2020 Navigating a product landscape for technology opportunity analysis: a word2vec approach using an integrated patent-product database. Technovation 9697, 102140; doi:10.1016/j.technovation.2020.102140.Google Scholar
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H. & Kang, J. 2020 BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 12341240; doi:10.1093/bioinformatics/btz682.Google ScholarPubMed
Lee, S., Kim, B., Huh, M., Cho, S., Park, S. & Lee, D. 2013 Mining transportation logs for understanding the after-assembly block manufacturing process in the shipbuilding industry. Expert Systems with Applications 40, 8395; doi:10.1016/j.eswa.2012.07.033.CrossRefGoogle Scholar
Lee, S., Mcadams, D. A. & Morris, E. 2017 Categorizing biological information based on function-morphology for bioinspired conceptual design. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 31, 359375; doi:10.1017/S0890060416000500.Google Scholar
Lee, W. S. & Sohn, S. Y. 2019 Discovering emerging business ideas based on crowdfunded software projects. Decision Support Systems 116, 102113; doi:10.1016/j.dss.2018.10.013.CrossRefGoogle Scholar
Lester, M., Guerrero, M. & Burge, J. 2020 Using evolutionary algorithms to select text features for mining design rationale. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 34, 132146; doi:10.1017/S0890060420000037.Google Scholar
Li, L., Qin, F., Gao, S. & Liu, Y. 2014 An approach for design rationale retrieval using ontology-aided indexing. Journal of Engineering Design 25, 259279; doi:10.1080/09544828.2014.969690.CrossRefGoogle Scholar
Li, S., Liu, F., Lu, H., Zhang, Y., Li, Y. & Yu, Z. 2021 Product family lean improvement based on matching deep mining of customer group preference. Research in Engineering Design 32, 469488; doi:10.1007/s00163-021-00367-8.CrossRefGoogle Scholar
Li, X., Chen, C.-H., Zheng, P., Jiang, Z. & Wang, L. 2021a A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design. Knowledge-Based Systems 215, 106739; doi:10.1016/j.knosys.2021.106739.CrossRefGoogle Scholar
Li, X., Chen, C.-H., Zheng, P., Wang, Z., Jiang, Z. & Jiang, Z. 2020 A knowledge graph-aided concept-knowledge approach for evolutionary smart product-service system development. Journal of Mechanical Design 142, 101403; doi:10.1115/1.4046807.CrossRefGoogle Scholar
Li, X., Lyu, M., Wang, Z., Chen, C.-H. & Zheng, P. 2021b Exploiting knowledge graphs in industrial products and services: a survey of key aspects, challenges, and future perspectives. Computers in Industry 129, 103449; doi:10.1016/j.compind.2021.103449.CrossRefGoogle Scholar
Li, Z. & Ramani, K. 2007 Ontology-based design information extraction and retrieval. AI EDAM 21, 137154.Google Scholar
Liang, Y., Liu, Y., Chen, C. & Jiang, Z. 2018 Extracting topic-sensitive content from textual documents—A hybrid topic model approach. Engineering Applications of Artificial Inteligence 70, 8191; doi:10.1016/j.engappai.2017.12.010.CrossRefGoogle Scholar
Liang, Y., Liu, Y., Kwong, C. K. & Lee, W. B. 2012 Learning the “Whys”: discovering design rationale using text mining—An algorithm perspective. Computer-Aided Design 44, 916930; doi:10.1016/j.cad.2011.08.002.CrossRefGoogle Scholar
Liao, T., Tanner, K. & MacDonald, E. F. 2020 Revealing insights of users’ perception: an approach to evaluate wearable products based on emotions. Design Science 6, e14; doi:10.1017/dsj.2020.7.CrossRefGoogle Scholar
Lim, S. & Tucker, C. S. 2016 A Bayesian sampling method for product feature extraction from large-scale textual data. Journal of Mechanical Design 138, 061403; doi:10.1115/1.4033238.CrossRefGoogle Scholar
Lin, H.-T., Chi, N.-W., Hsieh, S.-H., 2012. A concept-based information retrieval approach for engineering domain-specific technical documents. Advanced Engineering Informatics 26, 349360; doi:10.1016/j.aei.2011.12.003.CrossRefGoogle Scholar
Linsey, J., Markman, A. & Wood, K. 2012 Design by analogy: a study of the WordTree method for problem re-representation. Journal of Mechanical Design 134, 041009.CrossRefGoogle Scholar
Liu, Q., Wang, K., Li, Y. & Liu, Y. 2020 Data-driven concept network for inspiring designers’ idea generation. Journal of Computing and Information Science in Engineering 20, 031004; doi:10.1115/1.4046207.CrossRefGoogle Scholar
Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P., 2020. K-BERT: Enabling Language Representation with Knowledge Graph. AAAI 34, 2901–2908. Scholar
Liu, Y., Liang, Y., Kwong, C. K. & Lee, W. B. 2010 A new design rationale representation model for rationale mining. Journal of Computing and Information Science in Engineering 10, 031009; doi:10.1115/1.3470018.CrossRefGoogle Scholar
Longfan, L., Yana, L., Yan, X. & Cavallucci, D. 2020 A new function-based patent knowledge retrieval tool for conceptual design of innovative products. Computers in Industry 115, 103154; doi:10.1016/j.compind.2019.103154.Google Scholar
Lough, K. G., Van Wie, M., Stone, R. & Tumer, I. Y. 2009 Promoting risk communication in early design through linguistic analyses. Research in Engineering Design 20, 2940; doi:10.1007/s00163-009-0063-8.CrossRefGoogle Scholar
Luo, J., Sarica, S. & Wood, K. L. 2021 Guiding data-driven design ideation by knowledge distance. Knowledge-Based Systems 218, 106873; doi:10.1016/j.knosys.2021.106873.CrossRefGoogle Scholar
Luo, J., Song, B., Blessing, L. & Wood, K. 2018 Design opportunity conception using the total technology space map. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 449461; doi:10.1017/S0890060418000094.CrossRefGoogle Scholar
Luo, J., Yan, B. & Wood, K. 2017 InnoGPS for data-driven exploration of design opportunities and directions: the case of google driverless car project. Journal of Mechanical Design 139, 111416; doi:10.1115/1.4037680.CrossRefGoogle Scholar
Luong, T., Pham, H. & Manning, C. D. 2015 Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 14121421. Association for Computational Linguistics; doi:10.18653/v1/D15-1166.CrossRefGoogle Scholar
Maalej, W., Kurtanovic, Z., Nabil, H. & Stanik, C. 2016 On the automatic classification of app reviews. Requirements Engineering 21, 311331; doi:10.1007/s00766-016-0251-9.CrossRefGoogle Scholar
Madhusudanan, N., Chakrabarti, A. & Gurumoorthy, B. 2016 Discourse analysis based segregation of relevant document segments for knowledge acquisition. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 30, 446465; doi:10.1017/S0890060416000408.Google Scholar
Mansfield, N. J. 2016 Ranking of design journals based on results of the UK research excellence framework: using REF as referee. Design Journal 19, 903919; doi:10.1080/14606925.2016.1216212.CrossRefGoogle Scholar
Mele, M. & Campana, G. 2018 Prediction of Kansei engineering features for bottle design by a knowledge based system. International Journal of Interactive Design and Manufacturing (IJIDEM) 12, 12011210. doi:10.1007/s12008-018-0485-5.CrossRefGoogle Scholar
Menning, A., Grasnick, B. M., Ewald, B., Dobrigkeit, F. & Nicolai, C. 2018 Verbal focus shifts: forms of low coherent statements in design conversations. Design Studies 57, 135155; doi:10.1016/j.destud.2018.03.003.CrossRefGoogle Scholar
Menon, R., Tong, L. & Sathiyakeerthi, S. 2005 Analyzing textual databases using data mining to enable fast product development processes. Reliability Engineering & System Safety 88, 171180; doi:10.1016/j.ress.2004.07.007.CrossRefGoogle Scholar
Misaka, M. & Aoyama, H. 2018 Development of design system for crack patterns on cup surface based on KANSEI. Journal of Computational Design and Engineering 5, 435441; doi: 10.1016/j.jcde.2017.12.008.CrossRefGoogle Scholar
Moehrle, M. G. & Gerken, J. M. 2012 Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences. Scientometrics 91, 805826; doi:10.1007/s11192-012-0682-0.CrossRefGoogle Scholar
Moitra, A., Siu, K., Crapo, A. W., Durling, M., Li, M., Manolios, P., Meiners, M. & McMillan, C. 2019 Automating requirements analysis and test case generation. Requirements Engineering 24, 341364; doi:10.1007/s00766-019-00316-x.CrossRefGoogle Scholar
Montecchi, T., Russo, D. & Liu, Y. 2013 Searching in cooperative patent classification: comparison between keyword and concept-based search. Advanced Engineering Informatics 27, 335345. doi:10.1016/j.aei.2013.02.002.CrossRefGoogle Scholar
Morkos, B., Mathieson, J. & Summers, J. D. 2014 Comparative analysis of requirements change prediction models: manual, linguistic, and neural network. Research in Engineering Design 25, 139156. doi:10.1007/s00163-014-0170-z.CrossRefGoogle Scholar
Munoz, D. & Tucker, C. S. 2016 Modeling the semantic structure of textually derived learning content and its impact on recipients’ response states. Journal of Mechanical Design 138, 042001; doi:10.1115/1.4032398.CrossRefGoogle Scholar
Murphy, J., Fu, K., Otto, K., Yang, M., Jensen, D. & Wood, K. 2014 Function based design-by-analogy: a functional vector approach to analogical search. Journal of Mechanical Design 136, 101102.CrossRefGoogle Scholar
Nanda, J., Thevenot, H. J., Simpson, T. W., Stone, R. B., Bohm, M. & Shooter, S. B. 2007 Product family design knowledge representation, aggregation, reuse, and analysis. AI EDAM: Artificial Inteligence for Engineering Design Analysis and Manufacturing 21, 173192; doi:10.1017/S0890060407070217.Google Scholar
Noh, H., Jo, Y. & Lee, S. 2015 Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications 42, 43484360; doi:10.1016/j.eswa.2015.01.050.CrossRefGoogle Scholar
Pahl, G. & Beitz, W. 1988 Engineering design: a systematic approach. Nasa STI/RECON Technical Report A 89, 47350.Google Scholar
Pahl, G. & Beitz, W. 2013 Engineering Design: A Systematic Approach. Springer Science & Business Media.Google Scholar
Panchal, J. H. & Messer, M. 2011 Extracting the structure of design information from collaborative tagging. Journal of Computing and Information Science in Engineering 11, 041007; doi:10.1115/1.3617447.CrossRefGoogle Scholar
Pantel, P. & Pennacchiotti, M. 2006 Espresso: leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, ACL-44, pp. 113120. Association for Computational Linguistics; doi:10.3115/1220175.1220190.Google Scholar
Park, S.-M., Kim, Y.-G. & Baik, D.-K. 2016 Sentiment root cause analysis based on fuzzy formal concept analysis and fuzzy cognitive map. Journal of Computing and Information Science in Engineering 16, 031004; doi:10.1115/1.4034033.CrossRefGoogle Scholar
Park, Y. & Lee, S. 2011 How to design and utilize online customer center to support new product concept generation. Expert Systems with Applications 38, 1063810647; doi:10.1016/j.eswa.2011.02.125.CrossRefGoogle Scholar
Pimmler, T. U. & Eppinger, S. D. 1994 Integration Analysis of Product Decompositions. Massachusetts Institute of Technology (MIT), Sloan School of Management.CrossRefGoogle Scholar
Poggenpohl, S., Chayutsahakij, P. & Jeamsinkul, C. 2004 Language definition and its role in developing a design discourse. Design Studies 25, 579605; doi:10.1016/j.destud.2004.02.002.CrossRefGoogle Scholar
Prickett, P. & Aparicio, I. 2012 The development of a modified TRIZ technical system ontology. Computers in Industry 6