Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

  • Authors:

  • Muhammad Fahad;Nejib Moalla;Abdelaziz Bouras

  • Affiliations:

  • Decision & Information Sciences for Production Systems (DISP), CERRAL CENTER, University of Lyon2, Bron, France 69676;Decision & Information Sciences for Production Systems (DISP), CERRAL CENTER, University of Lyon2, Bron, France 69676;Decision & Information Sciences for Production Systems (DISP), CERRAL CENTER, University of Lyon2, Bron, France 69676

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2012

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Abstract

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from `circulatory error in class/property hierarchy', `common class between disjoint classes/properties', `redundancy of subclass/subproperty of relations' and other types of `semantic inconsistency' errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system's performance by producing only consistent accurate mappings, and reduce the users' dependability for ensuring the satisfiability of merged ontology.