Skip to main content
SpringerLink
Log in

Integrating Relation and Keyword Matching in Information Retrieval

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3684))

  • 1425 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amati, G., Ounis, I.: Conceptual Graphs and First Order Logic. The Computer Journal 43, 1 (2000)

    Article  MATH  Google Scholar 

  2. Chein, M., et Mugnier, M.-L.: Conceptual graphs are also graph. Research Report, 95-004, LIRMM (1995)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  4. Khoo, Christpoher, Soo-Guan: The Use of Relation matching in Information Retrieval. LIBRES: Library and Information Research 7, 2 (1997)

    Google Scholar 

  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. Liu, G.Z.: Semantic vector space model: Implementation and evaluation. Journal of American Society for Information Science 48(5), 395–417 (1997)

    Article  Google Scholar 

  7. Mittendorf, E., Mateev, B., Schäuble, P.: Using the co-occurrence of words for retrieval weighting. Information Retrieval 3, 243–251 (2000)

    Article  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  10. Mugnier, M.L.: On Generalization/Specialization for Conceptual Graphs. Journal of Experimental and theoretical Artificial Intelligence 7, 325–344 (1995)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  12. Savoy, J., Picard, P.: Retrieval effectiveness on the Web. Information Processing and Management 37(4), 643–669 (2001)

    Article  Google Scholar 

  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. Sowa, J.F.: Conceptual structures- Information processing in mind and machine. Addison-Wesley, Reading (1984)

    MATH  Google Scholar 

  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. Xu, Y., Benaroch, M.: Information retrieval with a hybrid automatic query expansion and data fusion procedure. Information Retrieval 7, 1–25 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. J.K. Institute of Applied Physics and Technology, Department of Electronics & Communication, University of Allahabad, Allahabad, 211002, India

    Tanveer J. Siddiqui

  2. Indian Institute of Information Technology, Allahabad, 211011

    Uma Shanker Tiwary

Authors
  1. Tanveer J. Siddiqui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uma Shanker Tiwary
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, Moulsecoomb, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Siddiqui, T.J., Tiwary, U.S. (2005). Integrating Relation and Keyword Matching in Information Retrieval. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_10

Download citation

  • DOI: https://doi.org/10.1007/11554028_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28897-8

  • Online ISBN: 978-3-540-31997-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation