Situated Cognition in the Semantic Web Era

  • Paul Compton
  • Byeong Ho Kang
  • Rodrigo Martinez-Bejar
  • Mamatha Rudrapatna
  • Arcot Sowmya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)


The challenge of situated cognition mounted by Clancey and others 20 years ago seems to have had little impact on the technical development of AI systems.  However, the hopes for the Semantic Web also seem far from being realised in much the same way as too much was expected of expert systems, and again this seems to be because of the situated nature of knowledge.  In this paper we claim that a possible way forward is to always ground the use of concepts in real data in particular contexts.  We base this claim on experience with Ripple-Down Rule systems.


Expert System Knowledge Acquisition Knowledge Engineering Unify Medical Language System Knowledge Engineer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Clancey, W.J.: Situated Cognition: On Human Knowledge and Computer Representations (Learning in Doing - Social, Cognitive and Computational Perspectives). Cambridge University Press, Cambridge (1997)Google Scholar
  2. Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W., Wielinga, B.: Knowledge Engineering and Management: The CommonKADS Methodology. MIT Press, Cambridge (1999)Google Scholar
  3. Studer, R., Benjamins, R., Fensel, D.: Knowledge engineering: principles and methods. Data and Knowledge Engineering 25(1-2), 161–197 (1998)zbMATHCrossRefGoogle Scholar
  4. Clancey, W.J.: A Perspective on the Nature of Artificial Intelligence - Enabling and Enhancing Capabilities for Society. In: AI 1988: Proceedings of the 2nd Australian Joint Artificial Intelligence Conference, pp. 2–6. Springer, Heidelberg (1990)Google Scholar
  5. Winograd, T., Flores, F.: Understanding computers and cognition. Addison-Wesley, Reading (1987)Google Scholar
  6. Dreyfus, H., Dreyfus, S.: Making a mind versus modelling the brain: artificial intelligence back at a branchpoint. Daedalus 117, 15–43 (1988) (Winter)Google Scholar
  7. Wittgenstein, L.: Philosophical Investigations. Blackwell, London (1953)Google Scholar
  8. Vera, A.H., Simon, H.A.: Situated cognition: A symbolic interpretation. Cognitive Science 17(1), 7–48 (1993)CrossRefGoogle Scholar
  9. Sandberg, J., Wielinga, B.: How situated is cognition. In: 12th International Joint Conference on Artificial Intelligence, pp. 341–346. Morgan Kauffman, Sydney (1991)Google Scholar
  10. Clancey, W.J.: Heuristic classification. Artificial Intelligence 27, 289–350 (1985)CrossRefGoogle Scholar
  11. Waterman, D.: A guide to expert systems. Addison Wesley, Reading (1986)Google Scholar
  12. Clancey, W.: Situated Action: A Neuropsychological Interpretation Response to Vera and Simon. Cognitive Science 17(1), 87–116 (1993)Google Scholar
  13. Compton, P.J., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241–257 (1990)CrossRefGoogle Scholar
  14. Kuhn, T.: The structure of scientific revolutions. The University of Chicago Press, Chicago (1962)Google Scholar
  15. Gangemi, A., Guarino, G., Masolo, C., Oltramari, A., Schneider, L.: Sweetening Ontologies with DOLCE. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 166–181. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. Rosenbloom, S.T., Miller, R.A., Johnson, K.B., Elkin, P.L., Brown, S.H.: Interface Terminologies: Facilitating Direct Entry of Clinical Data into Electronic Health Records. Journal of the American Medical Informatics Association 13, 277–288 (2006)CrossRefGoogle Scholar
  17. Powell, H., Lim, L.L.-Y., Heller, R.F.: Accuracy of administrative data to assess comorbidity in patients with heart disease: an Australian perspective. Journal of Clinical Epidemiology 52, 687–693 (2001)CrossRefGoogle Scholar
  18. Kim, M., Compton, P.: The perceived utility of standard ontologies in document management for specialized domains. International Journal of Human Computer Studies 64(1), 15–26 (2006)CrossRefGoogle Scholar
  19. Shaw, M.: Validation in a knowledge acquisition system with multiple experts. In: Proceedings of the International Conference on Fifth Generation Computer Systems, pp. 1259–1266 (1988)Google Scholar
  20. Horn, K., Compton, P.J., Lazarus, L., Quinlan, J.R.: An expert system for the interpretation of thyroid assays in a clinical laboratory. Australian Computer Journal 17(1), 7–11 (1985)Google Scholar
  21. Compton, P., Horn, R., Quinlan, R., Lazarus, L.: Maintaining an expert system. In: Quinlan, J.R. (ed.) Applications of Expert Systems, pp. 366–385. Addison Wesley, London (1989)Google Scholar
  22. Cao, T.M., Compton, P.: A Consistency-Based Approach to Knowledge Base Refinement. In: FLAIRS 2005: Proceedings of the 18th International Florida Artificial Intelligence Research Society, pp. 221–225. AAAI Press, Clearwater Beach (2005)Google Scholar
  23. Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: Proceedings of the 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, pp. 17.1-17.20. University of Calgary, Menlo Park (1995)Google Scholar
  24. Compton, P., Peters, L., Edwards, G., Lavers, T.G.: Experience with Ripple-Down Rules. Knowledge-Based System Journal 19(5), 356–362 (2006)CrossRefGoogle Scholar
  25. Compton, P., Jansen, R.: Knowledge in context: A strategy for expert system maintenance. In: Barter, C.J., Brooks, M.J. (eds.) AI 1988 (Proceedings of the 1988 Australian Artificial Intelligence Conference), pp. 292–306 (283-297 original Proceedings). Springer, Berlin (1989)Google Scholar
  26. Compton, P., Edwards, G., Kang, B., Lazarus, L., Malor, R., Menzies, T., Preston, P., Srinivasan, A., Sammut, C.: Ripple down rules: possibilities and limitations. In: 6th Banff AAAI Knowledge Acquisition for Knowledge Based Systems Workshop, Banff, vol. 18, pp. 6.1-6.18 (1991)Google Scholar
  27. Edwards, G., Compton, P., Malor, R., Srinivasan, A., Lazarus, L.: PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 25, 27–34 (1993)CrossRefGoogle Scholar
  28. Compton, P., Ramadan, Z., Preston, P., Le-Gia, T., Chellen, V., Mullholland, M.: A trade-off between domain knowledge and problem-solving method power. In: 11th Banff knowledge acquisition for knowledge-based systems workshop, pp. SHARE 17,1-19. SRDG Publications, University of Calgary, Banff (1998)Google Scholar
  29. Beydoun, G., Hoffmann, A.: Incremental Acquisition of Search Knowledge. International Journal of Human Computer Studies 52(3), 493–530 (2000)CrossRefGoogle Scholar
  30. Richards, D., Compton, P.: Revisiting Sisyphus I - an Incremental Approach to Resource Allocation Using Ripple-Down Rules. In: 12th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, pp. 7-7.1 - 7-7.20. SRDG Publications, University of Calgary, Banff (1999)Google Scholar
  31. Kang, B., Yoshida, K., Motoda, H., Compton, P.: A help desk system with intelligent interface. Applied Artificial Intelligence 11(7-8), 611–631 (1997)CrossRefGoogle Scholar
  32. Pham, S.B., Hoffmann, A.: Extracting Positive Attributions from Scientific Papers. In: Discovery Science: 7th International Conference, pp. 169–182. Springer, Padova (2004)Google Scholar
  33. Misra, A., Sowmya, A., Compton, P.: Incremental Learning of Control Knowledge for Lung Boundary Extraction. In: Proceedings of the Pacific Knowledge Acquisition Workshop 2004, pp. 211–225. University of Tasmania Eprints repository, Auckland (2004)Google Scholar
  34. Bekmann, J.P., Hoffmann, A.: Improved Knowledge Acquisition for High-Performance Heuristic Search. In: IJCAI 2005, Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 41–46. Edinburgh, Scotland (2005)Google Scholar
  35. Richards, D., Compton, P.: Taking Up the Situated Cognition Challenge with Ripple Down Rules. International Journal of Human Computer Studies 49, 895–926 (1998)CrossRefGoogle Scholar
  36. Rudrapatna, M., Sowmya, A.: Feature Weighted Minimum Distance Classifier with Multi-class Confidence Estimation. In: AI 2006: Advances in Artificial Intelligence, 19th Australian Joint Conference on Artificial Intelligence, pp. 253–263. Springer, Hobart, Tasmania (2006)Google Scholar
  37. Singh, P.: IC2:An Interval Based Characteristic Concept Learner. In: AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, pp. 950–953. Springer, Sydney (2005)Google Scholar
  38. Compton, P.J., Stuart, M.C., Lazarus, L.: Error in laboratory reference limits as shown in a collaborative quality assurance program. Clin. Chem. 32, 845–849 (1986)Google Scholar
  39. Pike, W., Gahegan, M.: Beyond ontologies: toward situated representations of scientific knowledge. International Journal of Human-Computer Studies 65, 659–673 (2007)CrossRefGoogle Scholar
  40. Latour, B.: Science in Action. Harvard University Press (1987)Google Scholar
  41. Schultze, U., Boland, R.J.: Knowledge management technology and the reproduction of knowledge work practices. Journal of Strategic Information Systems 9, 193–212 (2000)CrossRefGoogle Scholar
  42. Marcos, E., Marcos, A.: A philosophical approach to the concept of data model: is a data model, in fact, a model? Information Systems Frontiers 3, 267–274 (2001)CrossRefGoogle Scholar
  43. Berners-Lee, T., Hall, W., Hendler, J., Shadbolt, N., Weitzner, D.J.: Creating a science of the web. Science 313(5788), 769–771 (2006)CrossRefGoogle Scholar
  44. Sarraf, Q., Ellis, G.: Business Rules in Retail: The Story. Business Rules Journal 7(6) (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paul Compton
    • 1
  • Byeong Ho Kang
    • 2
  • Rodrigo Martinez-Bejar
    • 3
  • Mamatha Rudrapatna
    • 1
  • Arcot Sowmya
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesAustralia
  2. 2.School of ComputingUniversity of Tasmania, Sandy BayTasmaniaAustralia
  3. 3.KLT GroupUniversity of Murcia, Espinardo(Murcia)Spain

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