NFDI4DSO: Towards a BFO Compliant Ontology for Data Science
Genet Asefa Gesese, J\"org Waitelonis, Zongxiong Chen, Sonja, Schimmler, Harald Sack
TL;DR
This paper presents the development of the NFDI4DS Ontology, a BFO-compliant framework designed to improve data accessibility and interoperability in Data Science and AI by modeling resources and consortium structure.
Contribution
It introduces a new ontology based on NFDICore and BFO, facilitating FAIR data principles in Data Science and AI research.
Findings
Ontology models DS and AI resources effectively
Mapped to BFO for formal interoperability
Supports development of a comprehensive knowledge graph
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSemantic Web and Ontologies · Biomedical Text Mining and Ontologies · Data Mining Algorithms and Applications
MethodsOntology