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Semantic-Linguistic Feature Vectors for Search: Unsupervised Construction and Experimental Validation

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The Semantic Web (ASWC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5926))

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Abstract

In this paper, we elaborate on an approach to construction of semantic-linguistic feature vectors (FV) that are used in search. These FVs are built based on domain semantics encoded in an ontology and enhanced by a relevant terminology from Web documents. The value of this approach is twofold. First, it captures relevant semantics from an ontology, and second, it accounts for statistically significant collocations of other terms and phrases in relation to the ontology entities. The contribution of this paper is the FV construction process and its evaluation. Recommendations and lessons learnt are laid down.

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Author information

Authors and Affiliations

  1. Dept. of Computer and Information Science,  

    Stein L. Tomassen

  2. Dept. of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Norway

    Darijus Strasunskas

Authors
  1. Stein L. Tomassen
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  2. Darijus Strasunskas
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Editor information

Editors and Affiliations

  1. Facultad de Informática, Dpto. de Inteligencia Artificial, Ontology Engineering Group, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, 28660, Boadilla del Monte, Madrid

    Asunción Gómez-Pérez

  2. Shanghai Jiao Tong University, 200030, Shanghai, China

    Yong Yu

  3. Indiana University, USA

    Ying Ding

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Tomassen, S.L., Strasunskas, D. (2009). Semantic-Linguistic Feature Vectors for Search: Unsupervised Construction and Experimental Validation. In: Gómez-Pérez, A., Yu, Y., Ding, Y. (eds) The Semantic Web. ASWC 2009. Lecture Notes in Computer Science, vol 5926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10871-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-10871-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10870-9

  • Online ISBN: 978-3-642-10871-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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