NFDI4DSO: Towards a BFO Compliant Ontology for Data Science
This work addresses data management challenges for researchers in Data Science and AI, but it appears incremental as it builds on existing ontologies like NFDICore and BFO.
The paper tackles the problem of enhancing accessibility and interoperability of research data in Data Science and AI by introducing the NFDI4DS Ontology, which models resources and consortium structure to support FAIR principles and a knowledge graph.
The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To this end, this poster introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the NFDI4DS consortium. Built upon the NFDICore ontology and mapped to the Basic Formal Ontology (BFO), this ontology serves as the foundation for the NFDI4DS knowledge graph currently under development.