Toward the identification and elimination of semantic conflicts for the integration of RuleML-based ontologies

Integration of ontologies of information sources and consumers is important for achieving web-based interoperability and thus for the success of the Semantic Web as a whole. The present work describes an approach for eliminating semantic conflicts with the purpose of integrating ontologies of heterogeneous information sources. The paper is focused on elimination of homonymy and finding synonymy in ontologies of learning objects (namely course outlines) and identification of (in)compatibilities between course descriptions. As a proof of concept, ontologies are implemented using the XML-based Rule Markup Language (RuleML), which has been combined with the Web Ontology Language (OWL), a W3C standard, to form the Semantic Web Rule Language (SWRL). This representation in RuleML allows the ontology to be executable, flexibly extensible and platform-independent. The RuleML source representation can also easily be converted to other XML-based languages (such as RDF, OWL and SWRL) as well as incorporated into existing XML-based repositories (such as IEEE LOM and CanLOM) using XSL Transformations (XSLT). The facts and rules of the RuleML-based ontology are used by the OO jDREW reasoning engine to identify semantic homonymy and synonymy between components of course descriptions.