OM-2017: Proceedings of the Twelfth International Workshop on Ontology Matching

shvaiko2017aInternational audienceOntology matching is a key interoperability enabler for the semantic web, as well as auseful tactic in some classical data integration tasks dealing with the semantic heterogeneityproblem. It takes ontologies as input and determines as output an alignment,that is, a set of correspondences between the semantically related entities of those ontologies.These correspondences can be used for various tasks, such as ontology merging,data translation, query answering or navigation on the web of data. Thus, matchingontologies enables the knowledge and data expressed with the matched ontologies tointeroperate

Ontology matching for patent classification

Interdisciplinary research and development projects in medical engineering benefit from well selected collaboration partners. The process of finding such partners from often unfamiliar fields is difficult, but can be supported by an expert profile that is based on patent analysis and classifying the patents to competence fields in medical engineering. Patent analysis and categorization are difficult and require the analysis of the semantic content. Hence, we propose a twofold approach using a large controlled vocabulary, a smaller competence field ontology, and an alignment between them to assign patents to a certain competence field. The approach has two parts: a Topic Map approach and a Publication approach. We evaluate these approaches and its components in several ways. Furthermore, we compare four different ways to assign a patent to a competence field and show that the semantic wealth of a large biomedical ontology is beneficial to the classification task