Minds and Machines

, Volume 3, Issue 4, pp 421–451 | Cite as

Natural language processing using a propositional semantic network with structured variables

  • Syed S. Ali
  • Stuart C. Shapiro
General Articles


We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structures terms consisting of quantifier, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more “natural” formalism whose use and representations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenomena such as pronouns and ellipsis. Second, the formalism allows the specification of description subsumption as a partial ordering on related concepts (variable nodes in a semantic network) that relates more general concepts to more specific instances of that concept, as is done in language. Finally, this structured variable representation simplifies the resolution of some representational difficulties with certain classes of natural language sentences, namely, donkey sentences and sentences involving branching quantifiers. The implementation of this formalism is called ANALOG (A NAtural LOGIC) and its utility for natural language processing tasks is illustrated.

Key words

Natural Language Processing Knowledge Representation and Reasoning Semantic Networks Subsumption Quantifier Scoping Logic ANALOG 


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  1. 1.
    Syed, S. Ali. (1993), ‘A Structured Representation for Noun Phrases and Anaphora’, inProceedings of the Fifteenth Annual Conference of the Cognitive Science Society 197–202.Google Scholar
  2. 2.
    Syed, S. Ali. (1993),A “Natural Logic” for Natural Language Processing and Knowledge Representation. PhD thesis, State University of New York at Buffalo, Computer Science, 1993. Forthcoming.Google Scholar
  3. 3.
    Jon Barwise (1979), ‘On Branching Quantifiers in English’.J. Phil Logic 8, 47–80.Google Scholar
  4. 4.
    Jon Barwise and Robin Cooper (1981), ‘Generalized Quantifiers and Natural Language’,Linguistics and Philosophy 4, 159–219.Google Scholar
  5. 5.
    Daniel G. Bobrow and Terry Winograd (1977), ‘An Overview of KRL, a Knowledge Representation Language’,Cognitive Science 1(1), 3–46.Google Scholar
  6. 6.
    Ronald J. Brachman (1979), ‘On the Epistemological Status of Semantic Networks’, in N.V. Findler, editor,Associative Networks: Representation and Use of Knowledge in Computers, Academic Press, New York.Google Scholar
  7. 7.
    Ronald J. Brachman, Richard E. Fikes, and Hector J. Levesque (1985), ‘KRYPTON: a Functional Approach to Knowledge Representation’,IEEE Computer 16(10), 67–73.Google Scholar
  8. 8.
    Ronald J. Brachman, Victoria Pigman Gilbert, and Hector J. Levesque (1985), ‘An Essential Hybrid Reasoning System: Knowledge and Symbol Level Accounts of KRYPTON’,Proceedings IJCAI-85 1, 532–539.Google Scholar
  9. 9.
    Ronald J. Brachman and J. Schmolze (1977), An Overview of the KL-ONE Knowledge Representation System.Cognitive Science 9(2): 171–216.Google Scholar
  10. 10.
    Nick Cercone, Randy Goebel, John De Haan, and Stephanie Schaeffer (1992), ‘The ECO Family’,Computers and Mathematics with Applications 23(5), 95–131; Special issue on Semantic Networks in Artificial Intelligence (Part 1).Google Scholar
  11. 11.
    Sung-Hye Cho (1992), ‘Collections as Intensional Entities and Their Representations in a Semantic Network’, inProceedings of the Second Pacific Rim International Conference on Artifical Intelligence, pp. 388–394.Google Scholar
  12. 12.
    David R. Dowty, Robert E. Wall, and Stanley Peters (1981),Introduction to Montague Semantics, D. Reidel Publishing Co., Boston.Google Scholar
  13. 13.
    Scott E. Fahlman (1979),NETL: A System for Representing and Using Real-World Knowledge, MIT Press, Cambridge, MA.Google Scholar
  14. 14.
    G. Fauconnier (1978), Do Quantifiers Branch.Linguistic Inquiry 6(4), 555–578.Google Scholar
  15. 15.
    Kit Fine (1983), ‘A Defense of Arbitrary Objects’, inProceedings of the Aristotelian Society, volume suppl. vol. LVII, pp. 55–77.Google Scholar
  16. 16.
    Kit Fine (1985), “Natural Deduction and Arbitrary Objects’,Journal of Philosophical Logic, 14, 57–107.Google Scholar
  17. 17.
    Kit Fine (1985),Reasoning with Arbitrary Objects. Basil Blackwell, Oxford.Google Scholar
  18. 18.
    Peter Thomas Geach (1962),Reference and Generality. Cornell University Press, Ithaca, New York.Google Scholar
  19. 19.
    Robert Givan, David A. McAllester, and Sameer Shalaby (1991), Natural Language Based Inference Procedures Applied to Schubert's Steamroller’, inProceedings of AAAI-91, pp. 915–920.Google Scholar
  20. 20.
    Per-Kristian Halvorsen (1986), Natural Language Understanding and Montague Grammar.Computational Intelligence 2, 54–62.Google Scholar
  21. 21.
    Irene Heim (1990), Discourse Representation Theory. Tutorial material from ACL-90.Google Scholar
  22. 22.
    Gary G. Hendrix (1977), Expanding the Utility of Semantic Networks through Partitioning.Proc. 4th IJCAI.Google Scholar
  23. 23.
    Gary G. Hendrix (1979), ‘Encoding Knowledge in Partitioned Networks’, in N.V. Findler, Editor,Associative Networks: The Representation and Use of Knowledge in Computers. Academic Press, New York, pp 51–92.Google Scholar
  24. 24.
    L. Henkin (1961),Some Remarks on Infinitely Long Formulas Pergamon Press, Oxford, pp. 167–183.Google Scholar
  25. 25.
    J.R. Hobbs and S.M. Shieber (1987), An algorithm for generating quantifier scopings.Computation Linguistics 13(1–2), 47–63.Google Scholar
  26. 26.
    Hans Kamp (1984), ‘A Theory of Truth and Semantic Representation’, in Jeroen Groenendijk, Theo M.V. Janssen, and Martin Stokhof (eds.),Truth, Interpretation and Information, pp. 1–41. Forbis, Cinnaminson.Google Scholar
  27. 27.
    L.K. Schubert and F.J. Pelletier (1982), ‘From English to Logic: Context-free Computation of Conventional Logical Translation’,American Journal of Computational Linguistics 8, 165–176. Reprinted (with corrections) in B.J. Grosz, K. Sparck-Jones and B.L. Webber (eds.),Readings in Natural Language Processing, Morgan Kaufman, pp. 293–311.Google Scholar
  28. 28.
    J.W. Lloyd (1987),Foundations of Logic Programming Springer-Verlag, New York, 2nd Ed.Google Scholar
  29. 29.
    David A. McAllester (1989),Ontic: A Knowledge Representation System for Mathematics. MIT Press, Cambridge, MA.Google Scholar
  30. 30.
    David A. McAllester and Robert Givan (1992), ‘Natural Language Syntax and First-Order Inference’,Artificial Intelligence 56(10), 1–20.Google Scholar
  31. 31.
    Richard Montague (1973), ‘The Proper Treatment of Quantification in Ordinary English’, in J. Hintikka, J. Moravcsik, and P. Suppes, editors,Approaches to Natural Language, Riedel, Dordrecht, pp. 221–242. Also in R. Montague, 1974,Formal Philosophy: Selected Papers of Richard Montague, ed. by Richard Thomason, New Have: Yale University Press.Google Scholar
  32. 32.
    Richard Montague (1979), ‘English as a Formal Language’, in Richmond H. Thomason, editor,Formal Philosophy, Yale University Press, pp. 188–221.Google Scholar
  33. 33.
    W.V. Quine (1969),Ontological Relativity and Other Essays. Columbia University Press, London and New York.Google Scholar
  34. 34.
    W.V. Quine (1970),Philosophy of Logic. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  35. 35.
    Lenhart K. Schubert, Randolph G. Goebel, and Nicholas J. Cercone (1970), ‘The Structure and Organization of a Semantic Net for Comprehension and Inference’, in N.V. Findler, editor,Associative Networks: Representation and Use of Knowledge in Computers Academic Press, New York, pp. 121–175.Google Scholar
  36. 36.
    S.C. Shapiro (1979), ‘Generalized Augmented Transition Network Grammars for Generation From Semantic Networks’, InProceedings of the 17th Annual Meeting of the Association for Computational Linguistics, University of California at San Diego, pp. 25–29. Superseded by 34.Google Scholar
  37. 37.
    S.C. Shapiro (1982), Generalized Augmented Transition Network Grammars for Generation From Semantic Networks.The American Journal of Computational Linguistics 8(1), 12–25.Google Scholar
  38. 38.
    S.C. Shapiro (1986), Symmetric Relations, Intensional Individuals and Variable Binding.Proceedings of the IEEE,74(10), 1354–1363.Google Scholar
  39. 39.
    S.C. Shapiro (1991), Cables ‘Paths, and “Subconscious” Reasoning in Propositional Semantic Networks’, in John F. Sowa, editor,Principles of Semantic Networks, Morgan Kaufman, pp. 137–156.Google Scholar
  40. 40.
    S.C. Shapiro and W.J. Rapaport (1987), ‘SNePS considered as a fully intensional propositional semantic network’, in N. Cercone and G. McCalla, editors,The Knowledge Frontier. Springer-Verlag, New York, pp. 263–315.Google Scholar
  41. 41.
    S.C. Shapiro and William J. Rapaport (1987), (SNePS Considered as a Fully Intensional Propositional Semantic Network).Proceedings of the 5th National Conference on Artificial Intelligence 1, 278–283.Google Scholar
  42. 42.
    S.C. Shapiro and William J. Rapaport (1992), ‘The SNePS Family’,Computers and Mathematics with Applications 23(5), 243–275. Special issue on Semantic Networks in Artificial Intelligence (Part 1).Google Scholar
  43. 43.
    W.A. Woods (1978).Semantics and Quantification in Natural Language Question Answering, volume 17. Academic Press, New York.Google Scholar
  44. 44.
    William A. Woods (1991), ‘Understanding Subsumption and Taxonomy: A Framework for Progress’, in John F. Sowa, editor,Principles of Semantic Networks, Morgan Kaufmann, pp. 45–94.Google Scholar
  45. 45.
    William A. Woods and James G. Schmolze (1992), ‘The KL-ONE Family’,Computers and Mathematics with Applications 23(5), 133–177, Special issue on Semantic Networks in Artificial Intelligence (Part 1).Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Syed S. Ali
    • 1
  • Stuart C. Shapiro
    • 1
  1. 1.Department of Computer ScienceState University of New York at BuffaloBuffaloUSA

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