Alexander Borgida
Deborah L. McGuinness
Abstract
CLASSIC is a data model that encourages the description of objects not only in terms of their relations to other known objects, but in terms of a level of intensional structure as well. The CLASSIC language of structured descriptions permits i) partial descriptions of individuals, under an 'open world' assumption, ii) answers to queries either as extensional lists of values or as descriptions that necessarily hold of all possible answers, and iii) an easily extensible schema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of defining and populating the DB, as well as querying and answering.
CLASSIC (for which we have a prototype main-memory implementation) can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects.
The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This space of languages differs from the subsets of predicate calculus hitherto explored by deductive databases.
- 1 Abiteboul, S., and Hull, R. IFO: A formal semantic database model. A CM Trans. on Database Systems, 12(4):525-565, December 1987. Google Scholar
Digital Library
- 2 Agrawal, R., Borgida, A., and Jagadish, H. V. A transitive closure compression technique. To appear in Proc. ACM SIGMOD'89 Conference.Google Scholar
- 3 A~t-Kaci, H. A lattice-theoretic approach to computation based on a calculus of partially-ordered type structures. PhD thesis, University of Pennsylvania, 1984. Google ScholarDigital Library
- 4 Ait-Kaci, H., and Nasr, R. LOGIN: A logic programming language with built-in inheritance. Journal of Logic Programming, 3:187-215, 1986. Google ScholarDigital Library
- 5 Anderson, T. L., Ecklund, E., and Maier, D. PRO- TEUS: Objectifying the DBMS user interface. In Dittrich, K., and Dayal, U., editors, Proc. 1986 International Workshop on Object-Oriented Database Systems, Asilomar, CA, 1986. IEEE Computer Society Press. Google ScholarDigital Library
- 6 Bancilhon, F., and Ramakrishnan, R. An amateur's introduction to recursive query processing. In Proc. A CM SIGMOD'86 Conference, pages 16-52, May 1986. Google ScholarDigital Library
- 7 Beck, H. W., Gala, S. K., and Navathe, S. B. Classification as a query processing technique in the CANDIDE data model. In Proc. Fifth International Conference on Data Engineering, pages 572-581, Los Angeles, CA, 1989. Google ScholarDigital Library
- 8 Brachman, R. J., and Levesque, H. J. The tractability of subsumption in frame-based description languages. In Proc. AAAI-8$, pages 34-37, 1984.Google Scholar
- 9 Brachman, R. J., and Schmolze, J. An overview of the KL-ONE knowledge representation system. Cognitive Science, 9(2):171-216, April-June 1985.Google ScholarCross Ref
- 10 Brachman, R. J., Borgida, A., McGuinness, D. L., and Resnick, L. A. The CLASSIC knowledge representation system, or, KL-ONE: The next generation. In preprints of Workshop on Formal Aspects of Semantic Networks, Santa Catalina Island, California, February 1989.Google Scholar
- 11 Cholvy, L., and Demolombe, R. Querying a rule base. In Proc. First International Conference on Expert Database Systems, pages 365-371, 1986.Google Scholar
- 12 Devanbu, P. T., Selfridge, P. G., Ballard, B. W., and Brachman, R. J. Steps toward a knowledge-based software information system. Submitted to Eleventh InternationM Joint Conference on Artificial Intelligence, 1989.Google Scholar
- 13 Dittrich, K., mad Dayal, U., editors. Proc. International Workshop on Object-Oriented Database Systems, Pacific Grove, CA, September 1986. IEEE Computer Society Press. Google ScholarDigital Library
- 14 Gallaire, H., Minker, J., and Nicolas, J-M. Logic and databases: a deductive approach. A CM Computing Surve~ls, 16(2):153-186, June 1984. Google ScholarDigital Library
- 15 Goebel, R. G. The design and implementation of DLOG, a Prolog-based knowledge representation system. New Generation Computing, 3:385-401, 1985. Google ScholarDigital Library
- 16 Hull, R., and King, R. Semantic database modelling: survey, appfications, and research issues. A CM Computing Surveys, 19(3):210-260, September 1987. Google ScholarDigital Library
- 17 Imielinski, T. Incomplete deductive databases. Rutgers University. Unpubfished manuscript, 1987.Google Scholar
- 18 Imielinski, T. Intelligent query answering in rule based systems. Journal of Logic Programming, 4(3), September 1987. Google ScholarDigital Library
- 19 Levesque, H. J., and Brachman, R. J. Expressiveness and tractability in knowledge representation and reasoning. Computational Intelligence, 3(2):78-93, May 1987.Google ScholarCross Ref
- 20 MacGregor, R. F. A deductive pattern-matcher. In Proc. AAAI-88, pages 403-408, St. Paul, MN, August 1988.Google Scholar
- 21 Mark, L. Defining views in the binary relational model. information Systems, 12(3):281-294, 1987. Google ScholarDigital Library
- 22 Nebel, B. Computational complexity of terminological reasoning in BACK. Artificial Intelligence, 34(3):371- 383, April 1988. Google ScholarDigital Library
- 23 Patel-Schneider, P. F. Small can be beautiful in knowledge representation. In Proc. IEEE Workshop on Principles of Knowledge-Based Systems, pages 11-16, Denver, December 1984. A revised and extended version is available as AI Technical Report Number 37, Schlumberger Palo Alto Research, October 1984.Google Scholar
- 24 Patel-Schneider, P. F. Decidable, Logic-Based Knowledge Representation. PhD thesis, Department of Computer Science, University of Toronto, February 1987. A slightly revised version available as Technical Report Number 56, Schhmberger Palo Alto Research, May 1987 and as Technical Report 201/87, Department of Computer Science, University of Toronto. Google ScholarDigital Library
- 25 Patel-Schneider, P. F., Brachman, R. J., and Levesque, I-I. J. ARGON: Knowledge representation meets information retrieval. In Proc. First Conference on Artificial intelligence Applications, pages 280-286, 1984.Google Scholar
- 26 Reiter, R. On closed world databases. In Gallaire, H., and Minker, J., editors, Logic and Databases, pages 55- 76. Plenum Press, 1978.Google Scholar
- 27 Shipman, D. W. The functional data model and the data language DAPLEX. A CM Trans. on Database Systems, 6(1):140-173, March 1981. Google ScholarDigital Library
- 28 Shum, C. D., and Muntz, R. Implicit representation of extensional answers. In Proc. Second International Conference on Expert Database Systems, pages 257- 273, 1988.Google Scholar
- 29 yon Luck, K., Nebel, B., Peltason, C., and Schmiedel, A. The anatomy of the BACK system. KIT-Report 41, Technische Universitat Berlin, January 1987.Google Scholar
Index Terms
- CLASSIC: a structural data model for objects
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