UNASSIGNED: A survey showed European occupational scientists cover a broad range in occupational science (OS) research, however, no contemporary overviews of European OS research exists, and current research may provide valuable information for OS and occupational therapy.
UNASSIGNED: The aim was to provide an overview of contemporary European OS research.
UNASSIGNED: A scoping review was performed, including studies conducted in Europe and published in the British Journal of Occupational Therapy (BJOT), the Scandinavian Journal of Occupational Therapy (SJOT) or the Journal of Occupational Science (JOS) between 2015 and 2020. The journals were systematically searched, and quality assessment and thematic analysis were undertaken.
UNASSIGNED: Findings from 93 articles identified many studies from the Nordic countries. Most studies applied qualitative research methods. Theoretical concepts from OS were used in data generating and discussions. A wide range of demographics, and living conditions were explored. Recent articles took a reflexive stance on the positionality of the researcher/s.
UNASSIGNED: This review highlights the diversity of OS research, suggesting a solid theoretical knowledge base within European OS research.
UNASSIGNED: The results contribute to further development and maturation of the discipline of OS in Europe and internationally.

Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context.