Improving Information Retrieval Effectiveness by Using Domain Knowledge Stored in Ontologies

  • Gábor Nagypál
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3762)


The huge number of available documents on the Web makes finding relevant ones a challenging task. The quality of results that traditional full-text search engines provide is still not optimal for many types of user queries. Especially the vagueness of natural languages, abstract concepts, semantic relations and temporal issues are handled inadequately by full-text search. Ontologies and semantic metadata can provide a solution for these problems. This work examines how ontologies can be optimally exploited during the information retrieval process, and proposes a general framework which is based on ontology-supported semantic metadata generation and ontology-based query expansion. The framework can handle imperfect ontologies and metadata by combining results of simple heuristics, instead of relying on a “perfect” ontology. This allows integrating results from traditional full-text engines, and thus supports a gradual transition from classical full-text search engines to ontology-based ones.


Semantic Relation Query Expansion Vector Space Model Query Execution Test Collection 
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

  • Gábor Nagypál
    • 1
  1. 1.FZI Research Center for Information Technologies at the University of KarlsruheKarlsruheGermany

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