Automatic Computation of Semantic Proximity Using Taxonomic Knowledge

Part of the Studies in Computational Intelligence book series (SCI, volume 406)

Abstract

Taxonomic measures of semantic proximity allow us to compute the relatedness of two concepts. These metrics are versatile instruments required for diverse applications, e.g., the Semantic Web, linguistics, and also text mining. However, most approaches are only geared towards hand-crafted taxonomic dictionaries such as WORDNET, which only feature a limited fraction of real-world concepts. More specific concepts, and particularly instances of concepts, i.e., names of artists, locations, brand names, etc., are not covered.

The contributions of this paper are twofold. First, we introduce a framework based on Google and the Open Directory Project (ODP), enabling us to derive the semantic proximity between arbitrary concepts and instances. Second, we introduce a new taxonomy-driven proximity metric tailored for our framework. Studies with human subjects corroborate our hypothesis that our new metric outperforms benchmark semantic proximity metrics and comes close to human judgement.

Keywords

Semantic Similarity Automatic Computation Word Sense Human Rating Inverse Document Frequency 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.DBIS, Institut für InformatikUniversität FreiburgFreiburg i. Br.Germany

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