Towards mutual understanding: rule-based and learning-based matching algorithms for ontologies

Ontologies are formal, declarative knowledge representation models. They form a semantic foundation for many domains, such as Web services, E-commerce, service-oriented computing, and the Semantic Web. As the Semantic Web gains attention as the next generation of the Web, the importance of ontologies increases accordingly. However, because their designers have different conceptual views of the world, the resultant ontologies are heterogeneous. The heterogeneity can lead to misunderstandings, so there is a need for ontologies from different partners to be related and to reuse, wherever possible, each other's concepts. The availability of a global ontology can mitigate the heterogeneity, but it is infeasible, as verified by both theory and practice; an alternative manual matching process is time-consuming and error-prone, and cannot scale. Therefore, tools for ontology matching are in great need. However, performing ontology matching automatically is an extremely difficult task. Much research has been done on this topic and the suggested approaches can be categorized as either rule-based or learning-based. The former works on ontology schema information, and the latter considers both schemas and instance data. The approach described in this thesis makes six assumptions to bound the matching problem, and explains the assumptions and the bounds they provide. Then, three systems are presented towards the mutual reconciliation of concepts from different ontologies: (1) the Puzzle system belongs to the rule-based approach; (2) the SOCCER (Similar Ontology Concept ClustERing) system is mostly a learning-based solution, integrated with some rule-based techniques; and (3) the Compatibility Vector system, although not an ontology-matching algorithm by itself, instead is a means of measuring and maintaining ontology compatibility, which helps in the mutual understanding of ontologies and determines the compatibility of services (or agents) associated with ontologies.