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Using Context-Aware and Semantic Similarity Based Model to Enrich Ontology Concepts

  • Zenun Kastrati
  • Sule Yildirim Yayilgan
  • Ali Shariq Imran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9103)

Abstract

Domain ontologies are a good starting point to model in a formal way the basic vocabulary of a given domain. However, in order for an ontology to be usable in real applications, it has to be supplemented with lexical resources of this particular domain. The learning process of enriching domain ontologies with new lexical resources employed in the existing approaches takes into account only the contextual aspects of terms and does not consider their semantics. Therefore, this paper proposes a new objective metric namely SEMCON which combines contextual as well as semantic information of terms to enriching the domain ontology with new concepts. The SEMCON defines the context by first computing an observation matrix which exploits the statistical features such as frequency of the occurrence of a term, term’s font type and font size. The semantics is then incorporated by computing a semantic similarity score using lexical database WordNet. Subjective and objective experiments are conducted and results show an improved performance of SEMCON compared with tf*idf and \(\chi ^{2}\).

Keywords

Domain ontology Context aware Semantic similarity Concept 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zenun Kastrati
    • 1
  • Sule Yildirim Yayilgan
    • 1
  • Ali Shariq Imran
    • 1
  1. 1.Faculty of Computer Science and Media TechnologyGjøvik University CollegeGjovikNorway

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