Medical engineering (ME) is an interdisciplinary domain with short innovation cycles. Usually, researchers from several fields cooperate in ME research projects. To support the identification of suitable partners for a project, we present an integrated approach for patent classification combining ideas from topic modeling, ontology modeling & matching, bibliometric analysis, and data integration. First evaluation results show that the use of semantic technologies in patent classification can indeed increase the quality of the results.@en

Interdisciplinary research and development projects in medical engineering bene_t from well selected collaboration partners. The process of _nding such partners from often unfamiliar _elds is di_cult, but can be supported by an expert pro_le that is based on patent analysis and classifying the patents to competence _elds in medical engineering. Patent analysis and categorization are di_cult and require the analysis of the semantic content. Hence, we propose a twofold approach using a large controlled vocabulary, a smaller competence _eld ontology, and an alignment between them to assign patents to a certain competence _eld. The approach has two parts: a Topic Map approach and a Publi- cation approach. We evaluate these approaches and its components in several ways. Furthermore, we compare four di_erent ways to assign a patent to a competence _eld and show that the semantic wealth of a large biomedical ontology is bene_cial to the classi_cation task @en