Increasing digitalisation is leading to a constantly growing volume of data that comes from a wide variety of sources, formats and systems. In order to utilise this data efficiently, it must not only be available, but also understandable and combinable. This is precisely where our research in the field of semantics in data spaces comes in: We develop methods to enrich data with meaning, structure it and make it usable in a contextualised way.

Semantic modelling is a central element of our work. This involves not only describing data technically, but also interpreting its content - e.g. by defining terms, relations and units in so-called ontologies. These ontologies serve as a common understanding and enable the automatic linking and interpretation of information across system, specialist and organisational boundaries.

We investigate methods for the automated or assisted creation of semantic models in order to reduce time-consuming manual modelling. This includes algorithms for ontology extraction, semantic similarity analyses and methods for the continuous development and maintenance of knowledge models.

In addition, we design domain-specific data spaces in which semantically modelled data can be provided, processed and shared in a structured manner. These data spaces not only create interoperability, but also form the basis for further applications - for example in the field of artificial intelligence, data-driven decision support or automated analysis processes.

Our approaches are used in various areas, such as industry, mobility, healthcare, trade and the circular economy. We work closely with practice partners to develop concrete solutions for real challenges.