Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support

Ontology is increasingly seen as a key factor for enabling interoperability across heterogeneous systems and semantic web applications. Ontology mapping is required for combining distributed and heterogeneous ontologies. Developing such ontology mapping has been a core issue of recent ontology

Ontology mapping is important when working with more than one ontology. Typically similarity considerations are the basis for this. In this paper an approach to integrate various similarity methods is presented. In brief, we determine similarity through rules which have been encoded by ontology

Abstract. (Semi-)automatic mapping — also called (semi-)automatic alignment — of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here

Ontology is increasingly seen as a key factor for enabling interoperability across heterogeneous systems and semantic web applications. Ontology mapping is required for combining distributed and heterogeneous ontologies. Developing such ontology mapping has been a core issue of recent ontology

Abstract. A common approach to mitigate the effects of ontology heterogeneity is to discover and express the specific correspondences between different ontologies. An open research question is: how should such ontology mappings be represented. In recent years several proposals for an ontology

is to provide mechanised ways for mapping and merging ontologies. This has been the focus of recent research in knowledge engineering. However, we observe a dearth of mapping methods that are based on a strong theoretical ground, are easy to replicate in different settings, and use semantically-rich mechanisms

be resolved by mapping ontologies from diverse sources. Mapping large ontologies manually is almost impossible and results in a number of errors of omission and commission. Therefore, automated ontology mapping algorithms are a must. However, most existing ontology mapping tools do not provide exact mappings

Ontology-based cooperation of information systems: contributions to database-to-ontology mapping and XML-to-ontology mapping

Automatically discovering semantic relations between on-tologies is an important task with respect to overcoming se-mantic heterogeneity on the semantic web. Existing ontology matching systems, however, often produce erroneous map-pings. In this paper, we address the problem of errors in mappings