Using Background Knowledge in Schema Matching and Ontology Alignment

The use of background knowledge in schema matching is important as many matching tasks can be only solved with the use of additional background knowledge. For example, in the medical domain, thesauri and taxonomies are available which define concepts and terms in this domain. If two ontologies of this domain have to be aligned, using such information is required in order to identify similar elements as simpler techniques such as string matching and structural comparison are not sufficient. Furthermore, there is a lot of information available which could be used to improve matching results, however, there is currently no tool that can make use this information in a structured and organized way.

Therefore, the goal of this thesis is to develop a matcher which uses background information to identify correspondences in two models. This background information can include:

• (Domain-specific) ontologies, taxonomies, thesauri,
• Existing match results (i.e., correspondences or alignments),
• External documentation about the schemas or ontologies to be matched.

A first objective in this thesis is to identify possible source for background knowledge and to make that available in a structured way. In a second step, this structured information should be exploited by a matcher to identify correspondences of the models.

The matcher should be integrated into the schema matching framework of GeRoMeSuite. GeRoMeSuite is a generic model management system which includes already several schema matchers and provides the functionality to combine matchers to more complex matching strategies in a flexible manner. The matcher developed in this thesis should be integrated into such a strategy. The evaluation should show, that the matching strategy using background knowledge performs significantly better than strategies using no background knowledge.