Information Retrieval with Conceptual Graph Matching

  • Manuel Montes-y-Gómez
  • Aurelio López-López
  • Alexander Gelbukh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1873)

Abstract

The use of conceptual graphs for the representation of text contents in information retrieval is discussed. A method for measuring the similarity between two texts represented as conceptual graphs is presented. The method is based on well-known strategics of text comparison, such as Dice coefficient, with new elements introduced due to the bipartite nature of the conceptual graphs. Examples of the representation and comparison of the phrases are given. The structure of an information retrieval system using two-level document representation, traditional keywords and conceptual graphs, is presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Feldman, R., and I. Dagan (1995). “Knowledge Discovery in Textual databases (KDT)”. I91 International conference on Knowledge discovery (KDD_95), pp. 112–117, Montreal, 1995.Google Scholar
  2. 2.
    Genest D., and M. Chcin (1997). “An Experiment in Document Retrieval Using Conceptual Graphs”. Conceptual structures: Fulfilling Peirce’s Dream. LNAI1257, Springer, 1997.Google Scholar
  3. 3.
    Khoo, Christopher Soo-Guan (1997). “The Use of Relation Matching in Information Retrieval”. Electronic Journal ISSN 1058–6768, September 1997.Google Scholar
  4. 4.
    Lopez-Lopez, Aurelio, and Sung H. Myaeng (1996). “Extending the capabilities of retrieval systems by a two level representation of content”. Proceedings of the 1st Australian Document Computing Symposium, 1996.Google Scholar
  5. 5.
    Montes-y-Gómez, M., A. López-López, A. Gelbukh (1999a). “Text Mining as a Social Thermometer”. In Procs. Workshop on Text Mining: Foundations, Techniques and Applications, Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, August 1999.Google Scholar
  6. 6.
    Montcs-y-Gómcz, M., A. Gclbukh, A. López-López (1999b). “Document Title Patterns in Information Retrieval”, Proc. of the Workshop on Text, Speech and Dialogue TDS’99, Plzen, Czech Republic, September 1999.Google Scholar
  7. 7.
    Myaeng, Sung H. (1990). “Conceptual Graph Matching as a Plausible Inference Technique for Text Retrieval”. Proc. of the 5th Conceptual Structures Workshop, held in conjunction with A A AI-90, Boston, Ma, 1990.Google Scholar
  8. 8.
    Rasmusscn, Edic (1992). “Clustering Algorithms”. Information Retrieval: Data Structures & Algorithms. William B. Frakes and Ricardo Baeza-Yates (Eds)., Prentice Hall, 1992.Google Scholar
  9. 9.
    Salton, G. (1983). “Introduction to Modern Information Retrieval”. McGraw Hill, 1983.Google Scholar
  10. 10.
    Sowa, John F. (1983). “Conceptual Structures: Information Processing in Mind and Machine”. Ed. Addison-Wesley, 1983Google Scholar
  11. 11.
    Sowa, JohnF. (1999). “Knowledge Representation: Logical, Philosophical and Computational Foundations”. 1st edition, Thomson Learning, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Manuel Montes-y-Gómez
    • 1
  • Aurelio López-López
    • 2
  • Alexander Gelbukh
    • 1
  1. 1.Center for Computing Research (CIC)National Polytechnic Institute (IPN)DFMéxico
  2. 2.INAOEPueblaMexico

Personalised recommendations