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.

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.

Summary

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.

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).

Summary

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.

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.

Summary

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.

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.

Summary

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.

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 import.io) 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).

Summary

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.

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.

Miscellaneous

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.