Structured enrichment, modelling and networking of data with a context of meaning in order to enable their interoperability, findability and usability across system and domain boundaries in data spaces.

Specially customised and extended large language models that have been improved through domain-specific knowledge, additional training data or semantic structures in order to be used more precisely, comprehensibly and in a more application-oriented manner in specific contexts.

Our research in the field of semantic systems engineering aims to make knowledge usable in a structured way through the use of semantic technologies. This involves the use of ontologies, knowledge graphs, semantic modelling methods and interoperable data space architectures. This is complemented by the further development of large language models, which we optimise through fine-tuning, knowledge-based extensions and user-friendly interfaces for specific applications. In this way, we create data-driven systems that not only store information, but also understand it and provide it in context - as the basis for digital innovations in business, administration and society.

We work closely with industry partners, whether as part of publicly funded projects or direct R&D contracts. In doing so, we deal intensively with real challenges and always take the needs of end users and technical experts into account. This practical orientation ensures that our research results are not only theoretically sound, but also directly applicable in industrial practice and improve value creation.