The Phos Conceptual Language for Knowledge Representation

  • G. A. Vouros
  • C. D. Spyropoulos
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)


The role of a Knowledge Representation Language, as Brachman and Levesque described, is to support the explicit encoding of knowledge in a well specified way. Moreover, as they describe, “a representation system must provide access to facts implicit in the knowledge base. In other words, a representation component must perform automatic inferences for its users…”.


Functional Dependency Knowledge Representation User Requirement Individual Concept Conditional Dependency 
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]
    G. Abrett, M.H. Burstein, “The KREME knowledge editing environment”, Int.J. Man-Machine Studies 27, pp 103–126.Google Scholar
  2. [2]
    G. Berg_Gross, M. Price, “Acquiring and Managing Knowledge Using a Conceptual Structures Approach: Introduction and Framework” IEEE Transactions on Systems, Man and Cybernetics, Vol 19, No 3, pp 513–527 (1989)CrossRefGoogle Scholar
  3. [3]
    R.J. Brachman, “What’s in a concept:structural fundations for semantic networks”, Int.J. Man_Machine Studies, 9, pp 127–152 (1977)CrossRefGoogle Scholar
  4. [4]
    R.J. Brachman, H. Levesque, eds “Readings in Knowledge Representation”, Morgan Kaufmann 1985.Google Scholar
  5. [5]
    B. Chandrasekaran, M. Tanner, J. Josephson, “Explaining Control Strategies in Problem Solving” IEEE Expert, pp 9–24 (1989).Google Scholar
  6. [6]
    P. Cohen, J. DeLisio, D. Hart, “A Declarative Representation of Control Knowledge”, IEEE Trans. on Systems, Mam and Cybernetics, Vol 19, No 3, pp 546–557 (1989)CrossRefGoogle Scholar
  7. [7]
    R. Fikes, T. Kehler, “The Role of Frame_Based Representation in Reasoning”, Communications of the ACM, Vol 28, No 9, pp 904–920 (1985).CrossRefGoogle Scholar
  8. [8]
    M. Georgeff, A. Lansky, “Procedural Knowledge”, Proceedings of IEEE Vol 74, No 10, pp 1383–1398 (1986)CrossRefGoogle Scholar
  9. [9]
    H. Levesque, J. Mylopoulos, “A Procedural Semantics for Semantic Networks”, Editor N.V. Findler, Associative Networks, Academic Press pp 93–120 (1979).Google Scholar
  10. [10]
    Lenat, Feighanbaum, “On the thresholds of Knowledge”, Artificial Intelligence, 47, pp 185–250, (1991).MathSciNetCrossRefGoogle Scholar
  11. [11]
    M. Moser, “An overview of NIKL, the new implementation of KL-ONE”, Bolt Benarek and Newman Inc. Report No 4842, pp 233–260.Google Scholar
  12. [12]
    W.A. Woods, “Important Issues in Knowledge Representation”, Proceedings of the IEEE, Vol 74, No 10, pp 1322–1334 (1986).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • G. A. Vouros
    • 1
  • C. D. Spyropoulos
    • 1
  1. 1.Inst of Informatics and TelecommunicationsNCSR DEMOKRITOSAghia Paraskevi AttikisGreece

Personalised recommendations