Skip to main content
Log in

Am empirical analysis of optimization techniques for terminological representation systems

Or: Making KRIS get a move on

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

We consider different methods of optimizing the classification process of terminological representation systems and evaluate their effect on three different types of test data. Though these techniques can probably be found in many existing systems, until now there has been no coherent description of these techniques and their impact on the performance of a system. One goal of this article is to make such a description available for future implementors of terminological systems. Building the optimizations that came off best into theKRIS system greatly enhanced its efficiency.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R. J. Brachman and J. G. Schmolze, “An overview of the KL-ONE knowledge representation system,”Cogn. Sci. vol. 9, no. 2, pp. 171–216, April 1985.

    Google Scholar 

  2. R. J. Brachman,A Structural Paradigm for Representing Knowledge, Ph.D. thesis, Harvard University, 1977.

  3. C. Peltason, “The BACK system—an overview,”SIGART Bull. vol. 2, no. 3, pp. 114–119, June 1991.

    Google Scholar 

  4. P. F. Patel-Schneider, D. L. McGuinness, R. J. Brachman, L. Alperin Resnick, and A. Borgida, “The CLASSIC knowledge representation system: Guiding principles and implementation rational,”SIGART Bull. vol. 2, no. 3, pp. 108–113, June 1991.

    Google Scholar 

  5. P. F. Patel-Schneider, “Small can be beautiful in knowledge representation,” inProc. IEEE Workshop on Principles of Knowledge-Based Syst., Denver, CO, 1984, pp. 11–16. (An extended version including a KANDOR system description is available as AI Technical Report No. 37, Palo Alto, CA, Schlumberger Palo Alto Research, October 1984.)

    Google Scholar 

  6. M. B. Vilain, “The restricted language architecture of a hybrid representation system,” inProc. 9th Int. Joint Conf. Artif. Intell., Los Angeles, CA, August 1985, pp. 547–551.

  7. E. Mays, R. Dionne, and R. Weida, “K-Rep system overview,”SIGART Bull. vol. 2, no. 3, pp. 93–97, June 1991.

    Google Scholar 

  8. R. J. Brachman, V. Pigman Gilbert, and H. J. Levesque, “An essential hybrid reasoning system: Knowledge and symbol level accounts in KRYPTON,” inProc. 9th Int. Joint Conf. Artif. Intell., Los Angeles, CA, August 1985, pp. 532–539.

  9. F. Baader and B. Hollunder, “KRIS: Knowledge representation and inference system,”SIGART Bull. vol. 2, no. 3, pp. 8–14, June 1991.

    Google Scholar 

  10. R. MacGregor, “Inside the LOOM description classifier,”SIGART Bull. vol. 2, no. 3, pp. 88–92, June 1991.

    Google Scholar 

  11. J. Edelmann and B. Owsnicki, “Data models in knowledge representation systems: A case study,” inGWAI-86 und 2. Österreichische Artificial-Intelligence-Tagung edited by C.-R. Rollinger and W. Horn, Ottenstein, Austria, September 1986, pp. 69–74. Springer-Verlag: Berlin.

    Google Scholar 

  12. J. G. Schmolze and W. S. Mark, “The NIKL experience,”Comput. Intell. vol. 6, pp. 48–69, 1991.

    Google Scholar 

  13. A. Kobsa, “First experiences with the SB-ONE knowledge representation workbench in natural-language applications,”SIGART Bull. vol. 2, no. 3, pp. 70–76, June 1991.

    Google Scholar 

  14. R. Cattoni and E. Franconi, “Walking through the semantics of frame-based description languages: A case study,” inProc. Fifth Int. Symp. Methodol. Intell. Syst., Knoxville, TN, October 1990. North-Holland: Amsterdam, pp. 234–241.

  15. H. J. Levesque and R. J. Brachman, “Expressiveness and tractability in knowledge representation and reasoning,”Comput. Intell. vol. 3, pp. 78–93, 1987.

    Google Scholar 

  16. B. Nebel, “Computational complexity of terminological reasoning in BACK,”Artif. Intell. vol. 34, no. 3, pp. 371–383, April 1988.

    Google Scholar 

  17. M. Schmidt-Schauß, “Subsumption in KL-ONE is undecidable,” inPrinciples of Knowledge Representation and Reasoning: Proc. 1st Int. Conf. edited by R. Brachman, H. J. Levesque, and R. Reiter, Toronto, Ontario, May 1989, pp. 421–431. Morgan Kaufmann: San Mateo, CA.

    Google Scholar 

  18. P. F. Patel-Schneider, “Undecidability of subsumption in NIKL,”Artif. Intell. vol. 39, no. 2, pp. 263–272, June 1989.

    Google Scholar 

  19. B. Nebel, “Terminological reasoning is inherently intractable,”Artif. Intell. vol. 43, pp. 235–249, 1990.

    Google Scholar 

  20. M. Schmidt-Schauß and G. Smolka, “Attributive concept descriptions with complements,”Artif. Intell. vol. 48, pp. 1–26, 1991.

    Google Scholar 

  21. F. M. Donini, M. Lenzerini, D. Nardi, and W. Nutt, “The complexity of concept languages,” inPrinciples of Knowledge Representation and Reasoning: Proc. 2nd Int. Conf. edited by J. A. Allen, R. Fikes, and E. Sandewall, Cambridge, MA, April 1991, pp. 151–162. Morgan Kaufmann: San Mateo, CA.

    Google Scholar 

  22. F. M. Donini, M. Lenzerini, D. Nardi, and W. Nutt, “Tractable concept languages,” inProc. 12th Int. Joint Conf. Artif. Intell., Sydney, Australia, August 1991, pp. 458–465. Morgan Kaufmann: San Mateo, CA.

  23. J. Heinsohn, D. Kudenko, B. Nebel, and H.-J. Profitlich, “An empirical analysis of terminological representation systems,” inProc. 10th Nat. Conf. AAAI, San Jose, CA, July 1992, pp. 767–773. MIT Press: Cambridge, MA.

  24. J. Heinsohn, D. Kudenko, B. Nebel, and H.-J. Profitlich, “An empirical analysis of terminological representation systems,”Artif. Intell., 1993. To appear. (A preliminary version is available as DFKI Research Report RR-92-16.)

  25. T. Lipkis, “A KL-ONE classifier,” inProc. 1981 KL-ONE Workshop, edited by J. G. Schmolze and R. J. Brachman, Cambridge, MA, 1982, pp. 128–145. (The proceedings have been published as BBN Report No. 4842 and Fairchild Technical Report No. 618.)

  26. R. MacGregor, “A deductive pattern matcher,” inProc. 7th Nat. Conf. AAAI, Saint Paul, MI, August 1988, pp. 403–408.

  27. C. Peltason, A. Schmiedel, C. Kindermann, and J. Quantz, “The BACK system revisited.” Department of Computer Science, Technische Universität Berlin, Berlin, Germany, KIT Report 75, September 1989.

    Google Scholar 

  28. W. A. Woods, “Understanding subsumption and taxonomy: A framework for progress,” inPrinciples of Semantic Networks edited by J. F. Sowa, Morgan Kaufmann: San Mateo, CA, 1991, pp. 45–94.

    Google Scholar 

  29. R. A. Levinson, “A self-organizing pattern retrieval system for graphs,” inProc. 4th Nat. Conf. AAAI, Austin, TX, 1984, pp. 203–206.

  30. G. Ellis, “Compiled hierarchical retrieval.” In6th Annual Conceptual Graphs Workshop, 1991.

  31. R. A. Levinson, “Pattern associativity and the retrieval of semantic networks,”J Comput. Math. Appl. vol. 23, no. 6–9, pp. 573–600, 1992.

    Google Scholar 

  32. G. Ellis and R. A. Levinson, “The birth of PEIRCE: A conceptual graphs workbench,” inProc. Seventh Annu. Concept. Graphs Workshop, Las Cruces, NM, July 1992.

  33. F. Baader and B. Hollunder, “A terminological knowledge representation system with complete inference algorithms,” inInternational Workshop on Processing-Declaractive Knowledge edited by M. Richter and H. Boley, volume 567 ofLecture Notes in Artificial Intelligence Springer-Verlag: Berlin, 1991.

    Google Scholar 

  34. B. Nebel,Reasoning and Revision in Hybrid Representation Systems, volume 422 ofLecture Notes in Artificial Intelligence, Springer-Verlage: Berlin, Heidelberg, New York, 1990.

    Google Scholar 

  35. B. Hollunder, W. Nutt, and M. Schmidt-Schauß, “Subsumption algorithms for concept description languages,” inProc. 9th Eur. Conf. Artif. Intell., Stockholm, Sweden, August 1990, pp. 348–353, Pitman: New York.

  36. P. Winkler, “Random order,”Order vol. 1, pp. 317–331, 1985.

    Google Scholar 

  37. U. Faigle and G. Turàn, “Sorting and recognition problems for ordered sets,”SIAM J. Comput. vol. 17, no. 1, pp. 100–113, 1988.

    Google Scholar 

  38. M. Aigner,Combinatorical Search Teubner: Stuttgart, Germany, 1988.

    Google Scholar 

  39. D. Jungnickel,Graphen, Netzwerke und Algorithmen 2nd edition, Bl Wissenschaftsverlag: Mannheim, Germany, 1990.

    Google Scholar 

  40. H. V. Jagadish, “A compressed transitive closure technique for efficient fixed-point query processing,” inExpert Database Systems—Proc. 2nd Int. Conf. edited by L. Kerschberg, Menlo Park, CA, 1989, pp. 423–446. Benjamin/Cummings: Redwood City, CA.

    Google Scholar 

  41. T. L. Anderson, A. J. Berre, M. Mallison, H. H. Porter, III, and B. Schneider, “The hypermodel benchmark,” inProceedings of Extended Database Technology Springer-Verlag, Berlin, 1990.

    Google Scholar 

  42. R. G. G. Cattell and J. Skeen, “Object operations benchmark,”ACM Trans. Database Syst. vol. 17, no. 1, pp. 1–31, March 1992.

    Google Scholar 

  43. M. J. Carey, D. J. DeWitt, and J. F. Naughton, “The OO7 benchmark,” inProc. 1993 ACM SIGMOD Int. Conf. Management Data, Washington, DC, May 1993, pp. 12–21.

Download references

Author information

Authors and Affiliations

Authors

Additional information

This is a revised and extended version of a paper presented at the3rd International Conference on Principles of Knowledge Representation and Reasoning, October 1992, Cambridge, MA.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Baader, F., Hollunder, B., Nebel, B. et al. Am empirical analysis of optimization techniques for terminological representation systems. Appl Intell 4, 109–132 (1994). https://doi.org/10.1007/BF00872105

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00872105

Key words

Navigation