Matching of ontologies with XML schemas using a generic metamodel

  • Authors:

  • Christoph Quix;David Kensche;Xiang Li

  • Affiliations:

  • RWTH Aachen University, Informatik 5, Information Systems, Aachen, Germany;RWTH Aachen University, Informatik 5, Information Systems, Aachen, Germany;RWTH Aachen University, Informatik 5, Information Systems, Aachen, Germany

  • Venue:
  • OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
  • Year:
  • 2007

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Abstract

Schema matching is the task of automatically computing correspondences between schema elements. A multitude of schema matching approaches exists for various scenarios using syntactic, semantic, or instance information. The schema matching problem is aggravated by the fact that models to be matched are often represented in different modeling languages, e.g. OWL, XML Schema, or SQL DDL. Consequently, besides being able to match models in the same metamodel, a schema matching tool must be able to compute reasonable results when matching models in heterogeneous modeling languages. Therefore, we developed a matching component as a part of our model management system GeRoMeSuite which is based on our generic metamodel GeRoMe. As GeRoMe provides a unified representation of models, the matcher is able to match models represented in different languages with each other. In this paper, we will show in particular the results for matching XML Schemas with OWL ontologies as it is often required for the semantic annotation of existing XML data sources. GeRoMeSuite allows for flexible configuration of the matching system; various matching algorithms for element and structure level matching are provided and can be combined freely using different ways of aggregation and filtering in order to define new matching strategies. This makes the matcher highly configurable and extensible. We evaluated our system with several pairs of XML Schemas and OWL ontologies and compared the performance with results from other systems. The results are considerably better which shows that a matching system based on a generic metamodel is favorable for heterogeneous matching tasks.