Iterative Schema-based Semantic Matching

An ontology typically provides a vocabulary that describes a domain of interest and a specification of the meaning of terms used in the vocabulary. Depending on the precision of this specification, the notion of ontology encompasses several data and conceptual models, for example, classifications, database schemas, fully axiomatized theories. Ontologies tend to be put everywhere. They are viewed as the silver bullet for many applications, such as information integration, peer-to-peer systems, electronic commerce, semantic web services, social networks, and so on. They, indeed, are a practical means to conceptualize what is expressed in a computer format. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, just using ontologies does not reduce heterogeneity: it raises heterogeneity problems to a higher level. Ontology matching is a promising solution to the semantic heterogeneity problem. It finds correspondences between semantically related entities of the ontologies. These correspondences can be used for various tasks, such as ontology merging, query answering, data translation, or for navigation on the semantic web. Thus, matching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate. This dissertation focuses only on the task of discovering correspondences between various forms of ontologies with a particular consideration of classifications. Many various solutions of matching have been proposed so far. This work concentrates on a schema-based solution, namely a solution exploiting only the schema information, and not considering instance information. To ground the choice of the solution, this thesis provides a comprehensive coverage of the schema-based approaches used in ontology matching as well as their applications by reviewing state of the art in the field in a uniform way. It also points out how the approach developed in the thesis fits in with existing work. The thesis proposes the so-called semantic matching approach. This approach is based on two key ideas. The first is that correspondences are calculated between entities of ontologies by computing logical relations (e.g., equivalence, subsumption, disjointness), instead of computing coefficients rating match quality in the [0 1] range, as it is the case in many other approaches. The second idea is that the relations are determined by analyzing the meaning which is codified in the elements and the structures of ontologies. In particular, labels at nodes, written in natural language, are automatically translated into propositional formulas which explicitly codify the labels’ intended meaning. This allows the translation of the matching problem into a propositional validity problem, which can then be efficiently resolved using sound and complete state of the art propositional satisfiability deciders. The basic and iterative semantic matching algorithms as well as explanations of the correspondences produced have been designed and developed. The approach has been evaluated on various real world test cases with encouraging results, thus, proving empirically its benefits.