Flexible Comparison of Conceptual Graphs*

  • M. Montes-y-Gómez
  • A. Gelbukh
  • A. López-López
  • R. Baeza-Yates
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2113)

Abstract

Conceptual graphs allow for powerful and computationally affordable representation of the semantic contents of natural language texts. We propose a method of comparison (approximate matching) of conceptual graphs. The method takes into account synonymy and subtype/supertype relationships between the concepts and relations used in the conceptual graphs, thus allowing for greater flexibility of approximate matching. The method also allows the user to choose the desirable aspect of similarity in the cases when the two graphs can be generalized in different ways. The algorithm and examples of its application are presented. The results are potentially useful in a range of tasks requiring approximate semantic or another structural matching - among them, information retrieval and text mining.

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References

  1. 1.
    Agrawal, Rakesh, and Ramakrishnan Srikant (1994), “Fast Algorithms for Mining Association Rules”, Proc. 20th VLDB Conference, Santiago de Chile, 1994.Google Scholar
  2. 2.
    Ellis and Lehmann (1994), “Exploiting the Induced Order on Type-Labeled Graphs for fast Knowledge Retrieval”, Lecture Notes in Artificial Intelligence 835, Springer-Verlag 1994.Google Scholar
  3. 3.
    Genest D., and M. Chein (1997). “An Experiment in Document Retrieval Using Conceptual Graphs”. Conceptual structures: Fulfilling Peirce’s Dream. Lecture Notes in artificial Intelligence 1257, August 1997.Google Scholar
  4. 4.
    Huibers, Ounis and Chevallet (1996), “Conceptual Graph Aboutness”, Lecture Notes in Artificial Intelligence, Springer, 1996.Google Scholar
  5. 5.
    Marie, Marie (1995), “On generalization / specialization for conceptual graphs”, Journal of Experimental and Theoretical Artificial Intelligence, volume 7, pages 325–344, 1995.CrossRefGoogle Scholar
  6. 6.
    Myaeng, Sung H., and Aurelio López-López (1992), “Conceptual Graph Matching: a Flexible Algorithm and Experiments”, Journal of Experimental and Theoretical Artificial Intelligence, Vol. 4, 1992.Google Scholar
  7. 7.
    Myaeng, Sung H. (1992). “Using Conceptual graphs for Information Retrieval: A Framework for Adequate Representation and Flexible Inferencing”, Proc. of Symposium on Document Analysis and Information Retrieval, Las Vegas, 1992.Google Scholar
  8. 8.
    Rasmussen, Edie (1992). “Clustering Algorithms”. Information Retrieval: Data Structures & Algorithms. William B. Frakes and Ricardo Baeza-Yates (Eds.), Prentice Hall, 1992.Google Scholar
  9. 9.
    Sowa, John F. (1984). “Conceptual Structures: Information Processing in Mind and Machine”. Ed. Addison-Wesley, 1984.Google Scholar
  10. 10.
    Sowa, John F. (1999). “Knowledge Representation: Logical, Philosophical and Computational Foundations”. 1st edition, Thomson Learning, 1999.Google Scholar
  11. 11.
    Wu and Palmer (1994), “Verb Semantics and Lexical Selection”, Proc. of the 32nd Annual Meeting of the Associations for Computational Linguistics, 1994.Google Scholar
  12. 12.
    Yang, Choi and Oh (1992), “CGMA: A Novel Conceptual Graph Matching Algorithm”, Proc. of the 7th Conceptual Graphs Workshop, Las Cruces, NM, 1992.Google Scholar
  13. 13.
    Manuel Montes-y-Gómez, Alexander Gelbukh, Aurelio López-López (2000). Comparison of Conceptual Graphs. O. Cairo, L.E. Sucar, F.J. Cantu (eds.) MICAI 2000: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence N 1793, Springer-Verlag, pp. 548–556, 2000.Google Scholar
  14. 14.
    A.F. Gelbukh. “Review of R. Hausser’s ‘Foundations of Computational Linguistics: Man-Machine Communication in Natural Language’. ” Computational Linguistics, 26(3), 2000.Google Scholar
  15. 15.
    Manuel Montes-y-Gómez, Aurelio López-López, and Alexander Gelbukh. Information Retrieval with Conceptual Graph Matching. Proc. DEXA-2000, 11th International Conference on Database and Expert Systems Applications, Greenwich, England, September 4-8, 2000. Lecture Notes in Computer Science N 1873, Springer-Verlag, pp. 312–321.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • M. Montes-y-Gómez
    • 1
  • A. Gelbukh
    • 1
  • A. López-López
    • 2
  • R. Baeza-Yates
    • 3
  1. 1.Center for Computing Research (CIC)National Polytechnic Institute (IPN)Mexico
  2. 2.Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE)Mexico
  3. 3.Departamento de Ciencias de la ComputaciónUniversidad de ChileChile

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