Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-18T17:23:37.688Z Has data issue: false hasContentIssue false
  • English
  • Français

A methodology for engineering ontology acquisition and validation

Published online by Cambridge University Press:  16 December 2008

Zhanjun Li ,
Maria C. Yang  and
Karthik Ramani
Zhanjun Li
Affiliation:
Alibre Incorporated, Richardson, Texas, USA
Maria C. Yang
Affiliation:
Department of Mechanical Engineering and Engineering System Division, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Karthik Ramani
Affiliation:
School of Mechanical Engineering, and School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Current information retrieval approaches based on statistical methods and keyword matching are not effective in understanding the context of engineering content. They are not designed to be directly applicable to the engineering domain. Therefore, engineers have very limited means to harness and reuse past designs. The overall objective of our research is to develop an engineering ontology (EO)-based computational framework to structure unstructured engineering documents and achieve more effective information retrieval. This paper focuses on the method and process to acquire and validate the EO. The main contributions include a new, systematic, and more structured ontology development method assisted by a semiautomatic acquisition tool. This tool is integrated with Protégé ontology editing environment; an engineering lexicon (EL) that represents the associated lexical knowledge of the EO to bridge the gap between the concept space of the ontology and the word space of engineering documents and queries; the first large-scale EO and EL acquired from established knowledge resources for engineering information retrieval; and a comprehensive validation strategy and its implementations to justify the quality of the acquired EO. A search system based on the EO and EL has been developed and tested. The retrieval performance test further justifies the effectiveness of the EO and EL as well as the ontology development method.

Keywords

Engineering Information RetrievalEngineering OntologyKnowledge AcquisitionOntology Validation
Type
Research Article
Information
AI EDAM , Volume 23 , Issue 1: Developing and Using Engineering Ontologies , February 2009 , pp. 37 - 51
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahmed, S., Kim, S., & Wallace, K.M. (2007). A methodology for creating ontologies for engineering design. ASME Journal of Computer and Information Science in Engineering 7(2), 132–140.CrossRefGoogle Scholar
Baya, V., Gevins, J., Baudin, C., Mabogunje, A., Leifer, L., & Toye, G. (1992). An experimental study of design information reuse. Proc. 4th ASME/DTM Conf., pp. 141–147.Google Scholar
Borst, P., & Akkermans, H. (1997). Engineering ontologies. International Journal of Human–Computer Studies 46, 365–406.CrossRefGoogle Scholar
Brooke, D.V., Pennington, A.D., & Bloor, M.S. (1995). An ontology for engineering analysis. Engineering with Computers 11(1), 36–45.CrossRefGoogle Scholar
Ciocoiu, M., Nau, D.S., & Gruninger, M. (2001). Ontologies for integrating engineering applications. ASME Journal of Computing and Information Science in Engineering 1(1), 12–22.CrossRefGoogle Scholar
Court, A.W., Ullman, D.G., & Culley, S.J. (1998). A comparison between the provision of information to engineering designers in the UK and the USA. International Journal Information Management 18(6), 409–425.CrossRefGoogle Scholar
Dong, A., & Agogino, A.M. (1996). Text analysis for constructing design representations. Journal of Artificial Intelligence in Engineering 11, 65–75.CrossRefGoogle Scholar
Fernández-López, M., Gómez-Pérez, A., & Sierra, J.P. (1999). Building a chemical ontology using METHONTOLOGY and the ontology design environment. IEEE Intelligent Systems 14(1), 37–46.CrossRefGoogle Scholar
Gruber, T.R., & Olsen, G.R. (1994). An ontology for engineering mathematics. 4th Int. Conf. Principles of Knowledge Representation and Reasoning, Bonn, Germany.CrossRefGoogle Scholar
Gruber, T. (1995). Towards principles for the design of ontologies used for knowledge sharing. International Journal of Human–Computer Studies 43(5–6), 907–928.CrossRefGoogle Scholar
Grüninger, M., & Fox, M.S. (1995). Methodology for the design and evaluation of ontologies. Proc. Int. Joint Conf. AI Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal.Google Scholar
Grüninger, M., & Menzel, C. (2003). The process specification language (PSL) theory and applications. AI Magazine 24(3), 63–74.Google Scholar
Hertzum, M., & Pejtersen, A.M. (2000). The information-seeking practices of engineers: searching for document as well as for people. Journal of Information Processing and Management 36(5), 761–778.CrossRefGoogle Scholar
Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.CrossRefGoogle Scholar
Iyer, N., Lou, K., Jayanti, S., Kalyanaraman, Y., & Ramani, K. (2005). Shape-based searching for product lifecycle applications. Journal of Computer-Aided Design 37, 1435–1446.CrossRefGoogle Scholar
Jiang, J.J., & Conrath, D.W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. Proc. Int. Conf. Research on Computational Linguistics (ROCLING X), Taiwan.Google Scholar
Kim, J., Will, P., Ling, S.R., & Neches, B. (2003). Knowledge-rich catalog services for engineering design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(4), 349–366.CrossRefGoogle Scholar
Kitamura, Y., & Mizoguchi, R. (2004). Ontology-based systemization of functional knowledge. Engineering Design 15(4), 327–351.CrossRefGoogle Scholar
Kuffner, T.A., & Ullman, D.G. (1991). The information request of mechanical design engineers. Design Studies 12(1), 42–50.CrossRefGoogle Scholar
Lenat, D.B., & Guha, R.V. (1990). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Boston: Addison–Wesley.Google Scholar
Li, Z., & Ramani, K. (2007). Ontology-based design information extraction and retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(2), 137–154.CrossRefGoogle Scholar
Li, Z., Raskin, V., & Ramani, K. (2008). Developing engineering ontology for information retrieval. ASME Journal of Computing and Information Science in Engineering 8(1), 21–33.CrossRefGoogle Scholar
Lin, J., & Demner-Fushman, D. (2006). The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine. Proc. ACM SIGIR'06, pp. 99–106.CrossRefGoogle Scholar
Lin, J., Fox, M.S., & Bilgic, T. (1996). A requirement ontology for engineering design. Concurrent Engineering 4(3), 279–291.Google Scholar
Lohse, N., Hitendra, H., & Svetan, R. (2006). Equipment ontology for modular reconfigurable assembly systems. International Journal of Flexible Manufacturing Systems 17(4), 301–314.CrossRefGoogle Scholar
Lowe, A., McMahon, C., Shah, T., & Culley, S. (2000). An analysis of the content of technical information used by engineering designers. Proc. ASME/DET Conf., Baltimore, MD.CrossRefGoogle Scholar
Mayfield, J. (2002). Ontologies and text retrieval. The Knowledge Engineering Review 17(1), 71–75.CrossRefGoogle Scholar
McMahon, C.A., Lowe, A., Culley, S.J., Corderoy, M., Crossland, R., Shah, T., & Stewart, D. (2004). Waypoint: an integrated search and retrieval system for engineering documents. ASME Journal of Computing and Information Science in Engineering 4(4), 329–338.CrossRefGoogle Scholar
Nanda, J., Simpson, T.W., Kumara, S.R.T., & Shooter, S.B. (2006). A methodology for product family ontology development using formal concept analysis and web ontology language. ASME Journal of Computing and Information Science in Engineering 6(2), 1–11.CrossRefGoogle Scholar
Nirenburg, S., & Raskin, V. (2004). Ontological Semantics. Cambridge, MA: MIT Press.Google Scholar
Noy, N.F., & McGuinness, D.L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. Tech. Rep. KSL-01-05 and SMI-2001-0800. Stanford, CA: Stanford University, Knowledge Systems Laboratory and Stanford Medical Informatics.Google Scholar
Patil, L., Dutta, D., & Sriram, R. (2005 a). Ontology formalization of product semantics for product lifecycle management. Proc. ASME/IDETC CIE Conf., Long Beach, CA.CrossRefGoogle Scholar
Patil, L., Dutta, D., & Sriram, R. (2005 b). Ontology-based exchange of product data semantics. IEEE Trans. On Automation Science and Engineering 2(3), 213–225.CrossRefGoogle Scholar
Pugh, S. (1997). Total Design: Integrated Methods for Successful Product Engineering. Wokingham, MA: Addison–Wesley.Google Scholar
Resnik, P. (1999). Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity and natural language. Artificial Intelligence Research 11, 95–139.CrossRefGoogle Scholar
Salton, G. (1989). Automatic Text Processing. Wokingham, MA: Addison–Wesley.Google Scholar
Schlenoff, C., Denno, E., Ivester, R., Libes, D., & Szykman, S. (2000). An analysis and approach to using existing ontological systems for applications in manufacturing. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14(4), 257–270.CrossRefGoogle Scholar
Shamsfard, M., & Barforoush, A.A. (2004). Learning ontologies from natural language texts. International Journal of Human–Computer Studies 60, 17–63.CrossRefGoogle Scholar
Sim, S.K., & Duffy, A.H.B. (2003). Towards an ontology of generic engineering design activities. Research in Engineering Design 14(4), 200–223.CrossRefGoogle Scholar
Sudarsan, R., Fenves, S.J., Sriram, R.D., & Wang, F. (2005). A product information modeling framework for product lifecycle management. Journal of Computer-Aided Design 37(13), 1399–1411.CrossRefGoogle Scholar
Ullman, D.G. (2001). The Mechanical Design Process. New York: McGraw–Hill.Google Scholar
Uschold, M., & Grüninger, M. (2004). Ontologies and semantics for seamless connectivity. SIGMOD Record 33(4), 58–64.CrossRefGoogle Scholar
Uschold, M., & King, M. (1995). Towards a methodology for building ontologies. IJCAI95 Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal.Google Scholar
Witherell, P., Krishnamurty, S., & Grosse, I.R. (2007). Ontologies for supporting engineering design optimization. ASME Journal of Computing and Information Science in Engineering 7(2), 141–150.CrossRefGoogle Scholar
Yang, M.C., Wood, W.H., & Cutkosky, M.R. (2005). Design information retrieval: a thesauri-based approach for reuse of informal design information. Engineering with Computers 21(2), 177–192.CrossRefGoogle Scholar