Representing Knowledge in Dataspaces

Dataspaces are increasingly critical for enabling collaboration among diverse participants (individuals, institutions, or machines) in distributed environments. However, the effective sharing of data, knowledge, and services depends on a mutual understanding, which often remains poorly defined. Existing approaches focus on individual aspects of knowledge representation, but lack a holistic framework tailored to the requirements of dataspaces. This gap hinders seamless integration and mutual understanding across diverse participants, of both dataspace-specific and domain-specific content. This paper addresses these challenges by providing a clear definition of knowledge in the scope of dataspaces. We identify answers to key questions regarding why, what, where, and how knowledge needs to be represented, and provide an overview of essential knowledge representations needed for acting in dataspaces. In addition, we examine existing solutions from the Semantic Web and building blocks from the Dataspaces Support Centre (DSSC) Blueprint, highlighting gaps and opportunities for improvement. Our contributions aim to provide an overview of knowledge representations in dataspaces and a systematic foundation for future research and development in this area.