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OMEGA: An Automatic Ontology Metadata Generation Algorithm

  • Rachanee Ungrangsi
  • Elena Simperl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)

Abstract

At present, ontologies, independently of their location on the Web, are poorly documented, while additional descriptive information is spread across the Web in various forms, thus not being optimally accessible to potential users. State-of-the-art ontology location services offer a minimal set of search and navigation features, typically over a simple Web interface to the ontological resources administrated. Their retrieval functionality is significantly influenced by the absence of ontology metadata information. A first step towards the alleviation of this situation is the development of a feasible metadata schema for the systematic description of ontologies to allow a more effective access and exchange of ontologies across the Web. A second step, which is addressed in this paper, is the generation of the metadata information, particularly the mechanization of the process, as the only feasible means to scale at the number of ontologies constantly being developed in various sectors. We introduce OMEGA (Ontology MEtadata GenerAtion), an algorithm, which, as the name says, generates metadata about arbitrary ontologies on the Web in an automatic manner. The algorithm uses OMV (Ontology Metadata Vocabulary), as basic metadata schema capturing information about ontologies in a structured, machine-understandable form, and is available as Web application and REST Web service. It has been evaluated in terms of coverage, precision and recall, and overall user-perceived quality in several studies with positive results, providing thus a core building block for the realization of fully fledged ontology location services.

Keywords

Ontology Engineering Metadata Information Metadata Schema Core Ontology Metadata Element 
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

  • Rachanee Ungrangsi
    • 1
  • Elena Simperl
    • 2
  1. 1.Shinawatra UniversityPathumthaniThailand
  2. 2.STI Innsbruck, University of InnsbruckInnsbruckAustria

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