Summary of Nfdi4dso: Towards a Bfo Compliant Ontology For Data Science, by Genet Asefa Gesese et al.
NFDI4DSO: Towards a BFO Compliant Ontology for Data Science
by Genet Asefa Gesese, Jörg Waitelonis, Zongxiong Chen, Sonja Schimmler, Harald Sack
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The NFDI4DataScience project aims to improve data accessibility and interoperability within Data Science and Artificial Intelligence by linking digital artifacts and ensuring they follow FAIR principles. The project introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the consortium. Built upon the NFDICore ontology and mapped to Basic Formal Ontology (BFO), this ontology serves as the foundation for the knowledge graph currently under development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The NFDI4DataScience project makes data more accessible and useful by following special rules called FAIR principles. It’s like a big library where researchers can find and use each other’s work more easily. To make this happen, they created an “ontology” (like a blueprint) that helps organize and connect different types of digital artifacts in Data Science and Artificial Intelligence. |
Keywords
» Artificial intelligence » Knowledge graph
