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Pattern-Based Semantic Tagging for Ontology Population

  • Masumi Inaba
  • Takayuki Iida
  • Tomohiro Yamasaki
  • Kosei Fume
  • Yumiko Mizoguchi
  • Shinichi Nagano
  • Takahiro Kawamura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5006)

Abstract

Ontology population has emerged as an increasingly important problem in semantic web services. In this paper, we propose a method using named entity recognition that extracts keywords from Web pages in order to populate a product ontology. The semantic classification determines meanings of terms and phrases by heuristic rules after the morphological analysis. In addition, our method classifies vocabularies into different semantic tags. Firstly, it records several lists of semantic tags to a history database. Then, we define some rules from the lists to extract a product name. Finally, the rules build and refine the product ontology semi-automatically. According to an evaluation, proposed method achieved 87.1% precision and 87.4% recall. Thus, it can suggest some instances, and it decreases cost of updating the ontology.

Keywords

Semantic Classification Heuristic Rule Target Concept Entity Recognition Edition Rule 
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 2008

Authors and Affiliations

  • Masumi Inaba
    • 1
  • Takayuki Iida
    • 1
  • Tomohiro Yamasaki
    • 1
  • Kosei Fume
    • 1
  • Yumiko Mizoguchi
    • 1
  • Shinichi Nagano
    • 1
  • Takahiro Kawamura
    • 1
  1. 1.Corporate Research & Development CenterToshiba CorporationKanagawaJapan

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