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Integrating Relation and Keyword Matching in Information Retrieval

  • Tanveer J. Siddiqui
  • Uma Shanker Tiwary
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3684)

Abstract

We propose an information retrieval (IR) model that combines relation and keyword matching. The model relies on a novel algorithm for relation matching. The algorithm takes the advantage of any existing relational similarity between document and query to improve retrieval effectiveness. If query concepts(terms) appearing in a document exhibit similar relationship then the proposed similarity measure will give high rank to the document as compared to those in which query terms exhibit different relationship. A conceptual graph (CG) representation has been used to capture relationship between concepts. In order to keep the approach computationally simple a simplified form of CG matching has been used instead of graph derivation. Structural variations have been captured during matching through simple heuristics. CG similarity measure proposed by us is simple, flexible and scalable and can find application in many related tasks like information filtering, question answering, document summarization etc.

Keywords

Information Retrieval Retrieval Model Vector Space Model Conceptual Graph Concept Node 
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.

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References

  1. 1.
    Amati, G., Ounis, I.: Conceptual Graphs and First Order Logic. The Computer Journal 43, 1 (2000)zbMATHCrossRefGoogle Scholar
  2. 2.
    Chein, M., et Mugnier, M.-L.: Conceptual graphs are also graph. Research Report, 95-004, LIRMM (1995)Google Scholar
  3. 3.
    Khoo, C.S.G., Myaeng, S.H., Oddy, R.N.: Using cause-effect relations in text to improve information retrieval precision. Information Processing and Management 37, 119–145 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Khoo, Christpoher, Soo-Guan: The Use of Relation matching in Information Retrieval. LIBRES: Library and Information Research 7, 2 (1997)Google Scholar
  5. 5.
    Liddy, E.D., Myaeng, S.H.: DR-LINK: a system update for TREC-2. In: Second Text REtrieval Conference (TREC-2) (NIST-SP 500-215) NIST, Washington, DC, USA, pp. 85–99 (1994)Google Scholar
  6. 6.
    Liu, G.Z.: Semantic vector space model: Implementation and evaluation. Journal of American Society for Information Science 48(5), 395–417 (1997)CrossRefGoogle Scholar
  7. 7.
    Mittendorf, E., Mateev, B., Schäuble, P.: Using the co-occurrence of words for retrieval weighting. Information Retrieval 3, 243–251 (2000)zbMATHCrossRefGoogle Scholar
  8. 8.
    Montes-y-Gómez, M., López-López, A., Gelbukh, A.: Comparison of conceptual graphs. In: Cario, O., Sucar, L.E., Cantu, F.J. (eds.) MICAI 2000. LNCS, vol. 1793, pp. 548–556. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Montes-y-Gómez, M., Gelbukh, A., López-López, A., Baeza-Yates, R.: Flexible comparison of Conceptual Graphs. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, p. 102. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Mugnier, M.L.: On Generalization/Specialization for Conceptual Graphs. Journal of Experimental and theoretical Artificial Intelligence 7, 325–344 (1995)zbMATHCrossRefGoogle Scholar
  11. 11.
    Salton, G., Buckley, C., Smith, M.: On the application of syntactic methodologies in automatic text analysis. Information Processing and Management 26(1), 73–92 (1990)CrossRefGoogle Scholar
  12. 12.
    Savoy, J., Picard, P.: Retrieval effectiveness on the Web. Information Processing and Management 37(4), 643–669 (2001)CrossRefGoogle Scholar
  13. 13.
    Smeaton, A.F., O’Donnell, R., Kelledy, F.: Indexing structures derived from syntax in TREC-3: System description. In: Harman, D.K. (ed.) Overview of the Third Text REtrieval Conference (TREC-3) NIST SP-500-225, pp. 55–67. NIST, Gaithersburg (1995)Google Scholar
  14. 14.
    Sowa, J.F.: Conceptual structures- Information processing in mind and machine. Addison-Wesley, Reading (1984)zbMATHGoogle Scholar
  15. 15.
    Sparck Jones, K.: Summary performance comparisons TREC-2, TREC-3,TREC-4, TREC-5. In: TREC-5 Proceedings (1997), http://wwwnlpir.nist.gov/TREC/trec5.papers/sparckjones.ps
  16. 16.
    Xu, Y., Benaroch, M.: Information retrieval with a hybrid automatic query expansion and data fusion procedure. Information Retrieval 7, 1–25 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tanveer J. Siddiqui
    • 1
  • Uma Shanker Tiwary
    • 2
  1. 1.J.K. Institute of Applied Physics and Technology, Department of Electronics & CommunicationUniversity of AllahabadAllahabadIndia
  2. 2.Indian Institute of Information TechnologyAllahabad

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