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NFDI4DSO: Towards a BFO Compliant Ontology for Data Science

Genet Asefa Gesese, Jörg Waitelonis, Zongxiong Chen, Sonja Schimmler, Harald Sack

TL;DR

The paper addresses the challenge of interoperating metadata and artifacts across Data Science and AI consortia to improve findability, accessibility, interoperability, and reuse. It proposes a modular, BFO-aligned ontology, NFDI4DSO, built as an extension of NFDICore with additional classes, properties, SWRL rules, and role/process modeling, plus a knowledge-graph framework (RIG and RDG) for the NFDI4DS ecosystem. Key contributions include 42 new classes, 38 object properties, 9 data properties, 8 SWRL rules, and mappings to BFO and other ontologies, implemented in Protégé and documented with Widoco; a public first version of the NFDI4DS-KG demonstrates usage via SHMARQL/SPARQL queries. The work advances interoperability and domain integration for DS/AI resources and lays groundwork for cross-consortia data exchange and discovery through a scalable knowledge graph architecture.

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.

NFDI4DSO: Towards a BFO Compliant Ontology for Data Science

TL;DR

The paper addresses the challenge of interoperating metadata and artifacts across Data Science and AI consortia to improve findability, accessibility, interoperability, and reuse. It proposes a modular, BFO-aligned ontology, NFDI4DSO, built as an extension of NFDICore with additional classes, properties, SWRL rules, and role/process modeling, plus a knowledge-graph framework (RIG and RDG) for the NFDI4DS ecosystem. Key contributions include 42 new classes, 38 object properties, 9 data properties, 8 SWRL rules, and mappings to BFO and other ontologies, implemented in Protégé and documented with Widoco; a public first version of the NFDI4DS-KG demonstrates usage via SHMARQL/SPARQL queries. The work advances interoperability and domain integration for DS/AI resources and lays groundwork for cross-consortia data exchange and discovery through a scalable knowledge graph architecture.

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.
Paper Structure (5 sections, 3 figures)

This paper contains 5 sections, 3 figures.

Table of Contents

  1. Introduction
  2. The NFDI4DataScience Ontology (NFDI4DSO)
  3. Ontology Implementation
  4. NFDI4DSO in Use
  5. Conclusion and Future Work

Figures (3)

  • Figure 1: Example of representing roles where the prefixes ro and obi represent http://purl.obolibrary.org/obo/ro.owl and http://purl.obolibrary.org/obo/obi.owl ontologies, respectively.
  • Figure 2: A screenshot of part of the SHMARQL interface with the list of NFDI4DS co-spokespersons (refer to https://shorturl.at/eNb5e to navigate it fully.)
  • Figure 3: An example SPARQL query to provide a list of the co-spokespersons of the NFDI4DS Consortium. (It possible to query it live at: https://nfdi.fiz-karlsruhe.de/4ds/sparql)