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)


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.


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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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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