Skip to main content
SpringerLink
Log in

Ontology-guided knowledge retrieval in an automobile assembly environment

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In the body shop of an automobile assembly plant, having access to correct and timely information is very important in solving problems encountered in the assembly process. The Variation Reduction Adviser (VRA) system used within General Motors (GM) is a database containing problems encountered in this process and their possible solutions. The VRA acts as an electronic logbook that shares information across shifts within a plant as well as across multiple plants. The VRA also serves as a problem-solving tool by which solutions to problems encountered may be retrieved and reused. To function effectively as a problem-solving tool, it is important that relevant information is quickly retrieved from the VRA database. Traditionally, keyword-based retrieval strategies have been used. In these approaches, the user types in a list of words or phrases and those records in the database that contain those words or phrases exactly as typed are retrieved. The problem with this approach is that records containing words or phrases that are semantically related to the ones typed in but not exactly the same are not retrieved. For instance, if the user types “left-hand side,” the traditional keyword search will not find records that contain the abbreviation “LHS.” This paper describes a search mechanism based on a thesaurus (a simple version of an ontology) that overcomes this problem. It describes the standard criteria to measure the effectiveness of a search, defines a new criterion, and shows that in terms of these criteria, the ontology-guided approach gives better search results than the exact match mechanism. The results are shown in the context of real searches during the use of the VRA in a GM assembly plant.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59

    Google Scholar 

  2. Alizon F, Shooter S, Simpson T (2006) Reuse of manufacturing knowledge to facilitate platform-based product realization. Trans ASME J Comput Inf Sci Eng 6:170–178. doi:10.1115/1.2202135

    Article  Google Scholar 

  3. Becker M, Zirpoli F (2003) Knowledge integration in new product development: the FIAT Autocase. Int J Automot Technol Manage 3(1/2):30–46. doi:10.1504/IJATM.2003.003379

    Article  Google Scholar 

  4. Burkett W (2001) Product Data Markup Language: a new paradigm for product data exchange and integration. Computer-Aided Des 33(7):489–500. doi:10.1016/S0010-4485(01)00048-3

    Article  Google Scholar 

  5. Cafeo J, Gibbons D, Lesperance R, Morgan A, Sengir G, Simon A (2001) Capturing lessons learned for variation reduction in an automotive assembly plant. Proceedings of 14th International AI Research Society Conference. AAAI Press, Menlo Park, California, pp 89–92

  6. Ceglarek D, Shi J (1995) Dimensional variation reduction for automotive body assembly. Manuf Rev 8(2):139–154 (available at homepages.cae.wisc.edu/~darek/pdf_File/VarRed-MfgReview95.pdf)

    Google Scholar 

  7. Chougule R, Jalan M, Ravi B (2004) Casting knowledge management for concurrent casting product process design. Trans Am Foundry Soc 112:105–114

    Google Scholar 

  8. Choy K, Lee W, Lau H, Choy L (2005) A knowledge-based supplier intelligence retrieval system for outsource manufacturing. Knowl Base Syst 18:1–17. doi:10.1016/j.knosys.2004.05.003

    Article  Google Scholar 

  9. Ding Y, Foo S (2002) Ontology research and development: Part 1—a review of ontology generation. J Inf Sci 28(2):123–136

    Google Scholar 

  10. Fernandez-Lopez M (1999) Overview of methodologies for building ontologies. Proceedings of IJCAI-99 Workshop on Ontologies and Problem-Solving Methods: Lessons Learned and Future Trends, in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, August, Stockholm, Sweden, 1999

  11. Gebus S, Leiviskaa K (2009) Knowledge acquisition for decision support systems on an electronic assembly line. Expert Syst Appl 36(1):93–101. doi:10.1016/j.eswa.2007.09.058

    Article  Google Scholar 

  12. Golebiowska J, Dieng-Kuntz R, Corby O, Mousseau D (2001) Building and exploiting ontologies for an automobile project memory. Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), Victoria, Canada, October 22–23, 2001

  13. Grigorova D, Nikolov N (2007) Knowledge representation in systems with natural language interface. ACM International Conference Proceeding Series, Vol. 285, Proceedings of the 2007 international conference on Computer systems and technologies, Bulgaria, 2007

  14. Gruber T (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220. doi:10.1006/knac.1993.1008

    Article  Google Scholar 

  15. Hale P, Scanlan J, Bru C (2003) Design and prototyping of knowledge management software for aerospace manufacturing. 10th ISPE International Conference on Concurrent Engineering: Research and Applications, Madeira Island, Portugal, 26–30 July, 2003

  16. IBM (2005) Ontology definition metamodel, Third revised submission to OMG/ RFP. ad/2003-03-40. IBM, Sandpiper Software Inc.. http://www.omg.org/docs/ad/05-08-01.pdf

  17. Jing Y, Choi Y, Xiong Y, Han K, Shin S, Lee Y (2007) A Knowledge acquisition and management system for fault diagnosis and maintenance of equipments. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, Hangzhou, China, April 15–17, 2007

  18. Kuo R, Cha C, Chou S (2006) Developing a diagnostic system through integration of ant colony optimization systems and case based reasoning. Int J Adv Manuf Technol 30:750–760. doi:10.1007/s00170-005-0109-7

    Article  Google Scholar 

  19. Leake D, Birnbaum L, Hammond K, Marlow C, Yang H (1999) Integrating information resources: A case study of engineering design support. Third International Conference on Case-Based Reasoning, ICCBR-99, Germany, July 1999. Proceedings, Lecture Notes in Artificial Intelligence 1650, Springer-Verlag, Berlin, 1999, pp 482–496

  20. Lin H, Harding J, Teoh P (2005) An inter-enterprise semantic web system to support information autonomy and conflict moderation. Proc Inst Mech Eng B J Eng Manuf 219:903–911

    Article  Google Scholar 

  21. Modi S, Mabert V (2007) Supplier development: Improving supplier performance through knowledge transfer. J Oper Manage 25:42–64. doi:10.1016/j.jom.2006.02.001

    Article  Google Scholar 

  22. Morgan A, Cafeo J, Godden K, Lesperance R, Simon A, McGuinness D, Benedict J (2004) The General Motors Variation-Reduction Adviser: deployment issues for an AI application. Proceedings of the IAAI, July 2004

  23. Noy N, McGuinness D (2001) Ontology development 101: A guide to creating your first ontology. Report KSL-01-05, Knowledge Systems Laboratory, Stanford University, March 2001

  24. Olin J, Greis N, Kasarda J (1999) Knowledge management across multi-tier enterprises: the promise of intelligent software in the Auto industry. Eur Manage J 17(4):335–347. doi:10.1016/S0263-2373(99)00014-6

    Article  Google Scholar 

  25. Pouchard L, Ivezic N, Schlenoff C (2000) Ontology engineering for distributed collaboration in manufacturing. AI, Simulation and Planning–AIS’ 2000 Conference, March 6–8, 2000, Arizona, USA

  26. Saxena A, Wu B, Vachtsevanos G (2005) Integrated diagnosis and prognosis architecture for fleet vehicles using dynamic case based reasoning. Proceedings of the IEEE, Autotestcon 2005, pp 96–102

  27. Turng L, DeAugistine D (1999) A Web-based knowledge management system for the injection molding process. Plastics Eng December:47–50

  28. Yoshioka M, Umeda Y, Takeda H, Shimomura Y, Nomaguchi Y, Tomiyama T (2004) Physical concept ontology for the knowledge intensive engineering framework. Adv Eng Inform 18:95–113. doi:10.1016/j.aei.2004.09.004

    Article  Google Scholar 

  29. Zhang W, Yin J (2008) Exploring semantic web technologies for ontology-based modeling in collaborative engineering design. Int J Adv Manuf Technol 36:833–843. doi:10.1007/s00170-006-0896-5

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. General Motors R&D Center, Bangalore, India

    Sugato Chakrabarty & Rahul Chougule

  2. General Motors R&D Center, Warren, MI, USA

    Ronald M. Lesperance

  3. General Motors India Pvt. Limited, 2 nd Floor, Creator Building, International Technology Park, Whitefield Road, Bangalore, 560 066, India

    Sugato Chakrabarty

Authors
  1. Sugato Chakrabarty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul Chougule
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ronald M. Lesperance
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sugato Chakrabarty.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chakrabarty, S., Chougule, R. & Lesperance, R.M. Ontology-guided knowledge retrieval in an automobile assembly environment. Int J Adv Manuf Technol 44, 1237–1249 (2009). https://doi.org/10.1007/s00170-009-1925-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-1925-y

Keywords

Search

Navigation