An Analysis of the Origin of Ontology Mismatches on the Semantic Web

  • Authors:
  • Paul R. Smart;Paula C. Engelbrecht

  • Affiliations:
  • School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom SO17 1BJ;School of Psychology, Shackleton Building, University of Southampton, Southampton, United Kingdom SO17 1BJ

  • Venue:
  • EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
  • Year:
  • 2008

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Abstract

Despite the potential of domain ontologies to provide consensual representations of domain-relevant knowledge, the open, distributed and decentralized nature of the Semantic Web means that individuals will rarely, if ever, countenance a common set of terminological and representational commitments during the ontology design process. More often than not, differences between ontologies are likely to occur, and this is the case even when the ontologies describe identical or overlapping domains of interest. Differences between ontologies are often referred to as ontology mismatches and there is an extensive research literature geared towards the technology-mediated reconciliation of such mismatches. Our approach in the current paper is not to comment on the relative merits or demerits of the various technological solutions that could be used to resolve ontological differences; rather, we aim to explore the reasons why such differences may arise in the first place. In addition to a review of the various factors that contribute to ontology mismatches on the Semantic Web, we also discuss a number of focus areas for future research in this area. An improved understanding of the origins of ontology mismatches will, we argue, complement existing research into semantic integration techniques. In particular, by understanding more about the complex cognitive, epistemic and socio-cultural factors associated with the ontology development process, we may be able to develop knowledge acquisition and modeling tools/techniques that attenuate the impact of ontology mismatches for large-scale information sharing and data integration on the Semantic Web.