A Proposal for Integrating Artificial Intelligence and Database Techniques

  • Gian Piero Zarri


This paper describes some aspects of a current, interdisciplinary project carried out at the French National Center for Scientific Research (CNRS), and centered around a proposal conceming the integration of Artificial Intelligence (AI) and Database (DB) techniques. Our aim is to establish a sound methodology for the construction of Large Knowledge Based Systems (LKBSs) by combining the high-level information modeling features, the deductive capabilities, and the flexibility of the AI Knowledge Representation Languages (KRLs) with the characteristics of efficiency, security, concurrency, recovery and persistence of data provided by the Data Base Management Systems (DBMSs).We give in particular a succint description of the work already accomplished within the project in the knowledge representation area (set up of a powerful “hybrid representation system”).


Knowledge Representation Logic Programming Deductive Database Prolog Program Descriptive Component 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    AIT-KACI, H., and NASR, R. (1986) “LOGIN: A Logic Programming Language with Built-in Inheritance”, The Journal of Logic Programming, 3, 185–215.CrossRefGoogle Scholar
  2. [2]
    AL-ZOBAIDIE, A., and GRIMSON, J.B. (1987) “Expert Systems and Database Systems: How Can They Serve Each Other ?”, Expert Systems, 4, 30–37.Google Scholar
  3. [3]
    BRACHMAN, R.J. (1985) “ ‘I Lied about the Trees’ Or, Defaults and Definitions in Knowledge Representation”, AI Magazine, 6, n. 3, 80–93.Google Scholar
  4. [4]
    BRACHMAN, R.J., and SCHMOLZE, J.G. (1985) “An Overview of the KL-ONE Knowledge Representation System”, Cognitive Science, 9, 171–216.CrossRefGoogle Scholar
  5. [5]
    BRACHMAN, R.J., FIKES, R.E., and LEVESQUE, H.J. (1985) “KRYPTON: A Functional Approach to Knowledge Representation”, in Readings in Knowledge Representation. San Mateo: Morgan Kaufmann.Google Scholar
  6. [6]
    CERI, S., GOTTLOB, G., and TANCA, L. (1989) “What You Always Wanted to Know About DATALOG (And Never Dared to Ask)”, IEEE Transactions on Knowledge and Data Engineering, 1, 146–166.CrossRefGoogle Scholar
  7. [7]
    CHANG, C.L., and WALKER, A. (1986) “PROSQL: A PROLOG Programming Interface with SQL”, in Expert Database Systems — Proceedings from the First International Workshop, Kerschberg, L., ed. Menlo Park: Benjamin/Cummings.Google Scholar
  8. [8]
    DELCAMBRE, L.M.L., and ETHEREDGE, J.N. (1988) “A Self-Controlling Interpreter for the Relational Production Language”, in Proceedings of the 1988 SIGMOD Conference, special issue of ACM SIGMOD Record, 17, n. 3, 396–403.CrossRefGoogle Scholar
  9. [9]
    FIKES, R., and KEHLER, T. (1985) “The Role of Frame-Based Representation in Reasoning”, Communications of the ACM, 28, 904–920.CrossRefGoogle Scholar
  10. [10]
    GREINER, R., and LENAT, D.B. (1980) “A Representation Language Language”, in Proceedings of the First National Conference on Artificial Intelligence — AAAI/80. San Mateo: Morgan Kaufmann.Google Scholar
  11. [11]
    HULL, R., and KING, R. (1987) “Semantic Database Modeling: Survey, Applications, and Research Issues”, ACM Computing Surveys, 19, 201–260.CrossRefGoogle Scholar
  12. [12]
    LEUNG, Y.Y., and LEE, D.L. (1988) “Logic Approaches for Deductive Databases”, IEEE Expert, 3, n. 4, 64–75.CrossRefGoogle Scholar
  13. [13]
    NEWELL, A. (1982) “The Knowledgbe Level”, Artificial Intelligence, 18, 87–127.CrossRefGoogle Scholar
  14. [14]
    OZSOYOGLU, Z.M., ed. (1988) IEEE Data Engineering — Special Issue on Nested Relations, 11, n. 3.Google Scholar
  15. [15]
    POTTER, W.D., TRUEBLOOD, R.P., and EASTMAN, C.M. (1989) “Hyper-semantic Data Modeling”, Data and Knowledge Engineering, 4, 69–90.CrossRefGoogle Scholar
  16. [16]
    ROBINSON, J.A. (1965) “A Machine-Oriented Logic Based on the Resolution Principle”, Journal of the ACM, 12, 2341.CrossRefGoogle Scholar
  17. [17]
    TSUR, S. (1988) “LDL — A Technology for the Realization of Tightly Coupled Expert Systems”, IEEE Expert, 3, n. 3, 4151.CrossRefGoogle Scholar
  18. [18]
    VILAIN, M. (1985) “The Restricted Language Architecture of a Hybrid Representation System”, in Proceedings of the Ninth International Joint Conference on Artificial Intelligence — IJCAI/85. San Mateo: Morgan Kaufmann.Google Scholar
  19. [19]
    WIDOM, J., and FINKELSTEIN, S.J. (1989) A Syntax and Semantics for Set-Oriented Production Rules in Relational Database Systems (IBM Research Report). San Jose ( Calif. ): IBM Almaden Research Center.Google Scholar
  20. [20]
    ZARRI, G.P. (1989) “A Knowledge Representation Language for the Construction and Use of Large Intelligent Systems”, in Proceedings of the 1989 Annual Conference of the Associazione Italiana per l’Informatica ed il Calcolo Automatico (AICA). Milano: AICA.Google Scholar
  21. [21]
    ZARRI, G.P. (1990) “Temporal Knowledge Denotation in the Context of the Descriptive Component of a Knowledge Representation Language”, in Computational Intelligence, II, Gardin, F., Mauri, G., and Filippini, M., eds. Amsterdam: North-Holland.Google Scholar

Copyright information

© Springer-Verlag/Wien 1990

Authors and Affiliations

  • Gian Piero Zarri
    • 1
  1. 1.Centre National de la Recherche ScientifiqueCERTAL — INALCOParisFrance

Personalised recommendations