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Ontology-Supported Document Ranking for Novelty Search

  • Michael Färber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)

Abstract

Within specific domains, users generally face the challenge to populate an ontology according to their needs. Especially in case of novelty detection and forecast, the user wants to integrate novel information contained in natural text documents into his/her own ontology in order to utilise the knowledge base in a further step. In this paper, a semantic document ranking approach is proposed which serves as a prerequisite for ontology population. By using the underlying ontology for both query generation and document ranking, query and ranking are structured and, therefore, promise to provide a better ranking in terms of relevance and novelty than without using semantics.

Keywords

Document ranking Ontology-based information extraction Novelty detection Semantic similarity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Färber
    • 1
  1. 1.Karlsruhe Institute of Technology (KIT)KarlsruheGermany

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