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

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

As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.@en

Successful research and development projects start with finding the right partners for the venture. Especially for interdisciplinary projects, this is a difficult and tedious task as experts from foreign domains are not known. Fur thermore, the transfer of knowledge from research into practice is becoming more important in research projects to enable the quick application of research results. This is in particular relevant for projects in medical engineering. Patents and publications contain technical knowledge which can be exploited to find suitable experts. Patents are usually more product-oriented as the inventors have to describe an application area and products might be protected by patents. On the other hand, scientific publications represent the state-of-the-art in research. The challenge is finding the right mixture of research- or application-oriented experts from different domains. Hence, we propose a recommender system for finding experts for a certain topic based on patent topic clustering, ontologies, and ontology matching, which maps patents to corresponding innovation fields. The medical engineering domain serves as a first test bed, since projects in this area are highly interdisciplinary.@en

Successful research and development projects start with finding the right partners for the venture. Especially for interdisciplinary projects, this is a difficult task as experts from foreign domains are not known. Furthermore, the transfer of knowledge from research into practice is becoming more important in research projects to enable the quick application of research results. This is in particular relevant for projects in medical engineering. Patents and publications contain technical knowledge which can be exploited to find suitable experts. Patents are usually more product-oriented as the inventors have to describe an application area and products might be protected by patents. On the other hand, scientific publications represent the state-of-the-art in research. The challenge is finding the right mixture of research- or application-oriented experts from different domains. Hence, we propose a recommender system for experts for a certain topic based on patent topic clustering, ontologies, and ontology matching, which maps patents to corresponding innovation fields. The medical engineering domain serves as a first test bed, since projects in this area are highly interdisciplinary. @en