Knowledge Extraction for Information Retrieval

  • Francesco Corcoglioniti
  • Mauro Dragoni
  • Marco Rospocher
  • Alessio Palmero Aprosio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

Document retrieval is the task of returning relevant textual resources for a given user query. In this paper, we investigate whether the semantic analysis of the query and the documents, obtained exploiting state-of-the-art Natural Language Processing techniques (e.g., Entity Linking, Frame Detection) and Semantic Web resources (e.g., YAGO, DBpedia), can improve the performances of the traditional term-based similarity approach. Our experiments, conducted on a recently released document collection, show that Mean Average Precision (MAP) increases of 3.5 % points when combining textual and semantic analysis, thus suggesting that semantic content can effectively improve the performances of Information Retrieval systems.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francesco Corcoglioniti
    • 1
  • Mauro Dragoni
    • 1
  • Marco Rospocher
    • 1
  • Alessio Palmero Aprosio
    • 1
  1. 1.Fondazione Bruno KesslerTrentoItaly

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