Nowadays, many ontologies are used in industry, public adminstration and academia. Although these ontologies are developed for various purposes and domains, they often contain overlapping information. To build a collaborative semantic web, which allows data to be shared and reused across applications, enterprises, and community boundaries, it is necessary to find ways to compare, match and integrate various ontologies. Different strategies (e.g., string similarity, synonyms, structure similarity and based on instances) for determining similarity between entities are used in current ontology matching systems. Synonyms can help to solve the problem of using different terms in the ontologies for the same concept. The WordNet thesauri can support improving similarity measures. This paper provides an overview of how to apply WordNet in the ontology matching research area.

No abstract

Abstract. Semantic web technologies show great promise in usage scenarios that involve information logistics. This paper is an experience report on improving the semantic web ontology underlying an application used in expert finding. We use ontology design patterns to find and correct poor design choices, and align the application ontology to commonly used semantic web ontologies in order to increase the interoperability of the ontology and application. Lessons learned and problems faced are discussed, and possible future developments of the project mapped out.

Work presented in this paper addresses the challenge of bringing together concepts and experiences from two different areas of computer science: context modeling and ontology matching. Current work in the field of automatic ontology matching does not take into account the context of the user during the matching process. This paper is organized as follows, (1) we introduce of the concept of "context" in the ontology matching process, (2) we introduce the use cases and motivation along with an example of the use of context and (3) an approach for context-based semantic matching, which builds on different (weighted) levels of overlap for a better ranking of alignment elements depending on user's demand.

No abstract