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Enhanced SPARQL-based design rationale retrieval

Published online by Cambridge University Press:  04 October 2016

Luye Li ,
Shuming Gao ,
Ying Liu  and
Xiaolian Qin
Luye Li
Affiliation:
Nanjing Research Institute of Electronics Technology, Nanjing, China State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, China
Shuming Gao*
Affiliation:
State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, China
Ying Liu
Affiliation:
Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Cardiff, United Kingdom
Xiaolian Qin
Affiliation:
State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, China
*
Reprint requests to: Shuming Gao, State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, China. E-mail: smgao@cad.zju.edu.cn
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Abstract

Design rationale (DR) is an important category within design knowledge, and effective reuse of it depends on its successful retrieval. In this paper, an ontology-based DR retrieval approach is presented, which allows users to search by entering normal queries such as questions in natural language. First, an ontology-based semantic model of DR is developed based on the extended issue-based information system-based DR representation in order to effectively utilize the semantics embedded in DR, and a database of ontology-based DR is constructed, which supports SPARQL queries. Second, two SPARQL query generation methods are proposed. The first method generates initial SPARQL queries from natural language queries automatically using template matching, and the other generates initial SPARQL queries automatically from DR record-based queries. In addition, keyword extension and optimization is conducted to enhance the SPARQL-based retrieval. Third, a design rationale retrieval prototype system is implemented. The experimental results show the advantages of the proposed approach.

Keywords

Design Rationale RetrievalOntologySPARQL TemplateText Search

Information

Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 4: Engineering Design Informatics , November 2016 , pp. 406 - 423
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Ahmed, S., & Wallace, K.M. (2004). Understanding the knowledge needs of novice designers in the aerospace industry. Design Studies 25(2), 155–173.CrossRefGoogle Scholar
Bracewell, R.H., Wallace, K.M., Moss, M., & Knott, D. (2009). Capturing design rationale. Computer-Aided Design 41(3), 173–186.Google Scholar
Burge, J.E., & Brown, D.C. (2008). Software engineering using RATionale. Journal of Systems and Software 81(3), 395–413.Google Scholar
Conklin, J., & Begeman, M.L. (1988). gIBIS: a hypertext tool for exploratory policy discussion. Design Studies 12(4), 303–331.Google Scholar
De Medeiros, A.P., & Schwabe, D. (2008). Kuaba approach: integrating formal semantics and design rationale representation to support design reuse. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(4), 399–419.Google Scholar
Fenves, S.J., Foufou, S., Bock, C., & Sriram, R.D. (2008). A core model for product data. Journal of Computing and Information Science in Engineering 8(1), 014501.CrossRefGoogle Scholar
Gruber, T.R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220.Google Scholar
Hirtz, J., Stone, R.B., McAdams, D.A., Szykman, S., & Wood, K.L. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.Google Scholar
Kara, S., Alan, Ö., Sabuncu, O., Akpınar, S., Cicekli, N.K., & Alpaslan, F.N. (2012). An ontology-based retrieval system using semantic indexing. Information Systems 37(4), 294–305.CrossRefGoogle Scholar
Kim, S., Bracewell, R.H., & Wallace, K.M. (2005). A framework for design rationale retrieval. Proc. Int. Conf. Engineering Design, ICED'05. Melbourne, Australia: Design Society.Google Scholar
Kim, S., Bracewell, R.H., & Wallace, K. M. (2007). Improving design reuse using context. Proc. Int. Conf. Engineering Design, ICED'07. Paris: Design Society.Google Scholar
Kolomiyets, O., & Moens, M.-F. (2011). A survey on question answering technology from an information retrieval perspective. Information Sciences 181(24), 5412–5434.CrossRefGoogle Scholar
Kunz, W., & Rittel, H.W.J. (1970). Issues as Elements of Information Systems. Berkeley, CA: University of California at Berkeley.Google Scholar
Li, L., Qin, F., & Gao, S. (2013). An extended design rationale representation for supporting retrieval and reuse of design knowledge. Journal of Computer-Aided Design & Computer Graphics (Chinese Journal) 25(10), 1514–1522.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(7–9), 259–279.Google Scholar
Liang, Y., Liu, Y., Kwong, C.K., & Lee, K.B. (2012). Learning the “Whys”: discovering design rationale using text mining—an algorithm perspective. Computer-Aided Design 44(10), 916–930.Google Scholar
Liang, Y., Lu, W.F., Liu, Y., & Lim, S.C.J. (2010). Interactive interface design for design rationale search and retrieval. Proc. ASME 2010 IDETC & CIE Conf., Montreal.Google Scholar
Lim, S.C.J., Liu, Y., & Lee, W.B. (2010). Multi-facet product information search and retrieval using semantically annotated product family ontology. Information Processing & Management 46(4), 479–493.Google Scholar
Lim, S.C.J., Liu, Y., & Lee, W.B. (2011). A methodology for building a semantically annotated multi-facet ontology for product family modelling. Advanced Engineering Informatics 25(2), 147–161.Google Scholar
Liu, J., & Hu, X. (2013). A reuse oriented representation model for capturing and formalizing the evolving design rationale. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(4), 401–413.CrossRefGoogle 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(3), 031009.CrossRefGoogle Scholar
López, C., Cysneiros, L.M., & Astudillo, H. (2008). NDR ontology: sharing and reusing NFR and design rationale knowledge. Proc. 1st Int. Workshop on Managing Requirements Knowledge, MARK'08. Barcelona: IEEE.Google Scholar
MacLean, A., Young, R.M., Bellotti, V.M.E., & Moran, T.P. (1991). Questions, options, and criteria: elements of design space analysis. Human-Computer Interaction 6(3), 201–250.Google Scholar
McCall, R.J. (1991). PHI: a conceptual foundation for design hypermedia. ACM Transactions on Office Information Systems 6(1), 30–41.Google Scholar
Minack, E., Sauermann, L., Grimnes, G., Fluit, C., & Broekstra, J. (2008). The Sesame LuceneSail: RDF queries with full-text search. Technical Report 1, NEPOMUK Consortium.Google Scholar
Pahl, G., Wallace, K., & Blessing, L. (2007). Engineering Design: A Systematic Approach, Vol. 157. Berlin: Springer.Google Scholar
Qin, F., Li, L., & Gao, S. (2012). A survey of design rationale. Journal of Computer-Aided Design & Computer Graphics (Chinese Journal) 24(10), 1283–1293.Google Scholar
Regli, W.C., Hu, X., Atwood, M., & Sun, W. (2000). A survey of design rationale systems: approaches, representation, capture and retrieval. Engineering With Computers 16(3–4), 209–235.Google Scholar
Rockwell, J., Grosse, I.R., Krishnamurty, S., & Wileden, J.C. (2009). A decision support ontology for collaborative decision making in engineering design. Proc. 2009 Int. Symp. Collaborative Technologies and Systems, pp. 1–9, Baltimore, MD, May 18–22.CrossRefGoogle Scholar
Štorga, M., Andreasen, M.M., & Marjanović, D. (2010). The design ontology: foundation for the design knowledge exchange and management. Journal of Engineering Design 21(4), 427–454.Google Scholar
Unger, C., Bϋhmann, L., Lehmann, J., Ngonga Ngomo, A.-C., Gerber, D., & Ciminao, P. (2012). Template-based question answering over RDF data. Proc. 21st Int. Conf. World Wide Web, pp. 639–648. New York: ACM.Google Scholar
Wang, H., Johonson, A., & Bracewell, R.H. (2009). Supporting design rationale retrieval for design knowledge reuse. Proc. Int. Conf. Engineering Design, ICET'09. Stanford, CA: Design Society.Google Scholar
Wang, H., Johnson, A.L., & Bracewell, R.H. (2012). The retrieval of structured design rationale for the re-use of design knowledge with an integrated representation. Advanced Engineering Informatics 26(2), 251–266.Google Scholar
Zhang, Y., Luo, X., Li, J., & Buis, J.J. (2013). A semantic representation model for design rationale of products. Advanced Engineering Informatics 27(1), 13–26.Google Scholar
Zhu, L., Jayaram, U., Jayaram, S., & Kim, O. (2010). Querying and reasoning with product engineering ontologies—moving past modeling. Proc. ASME 2010 IDETC&CIE Conf., Montreal.Google Scholar