Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions
Kanishka Silva, Marcel R. Ackermann, Heike Fliegl, Genet-Asefa Gesese, Fidan Limani, Philipp Mayr, Peter Mutschke, Allard Oelen, Muhammad Asif Suryani, Sharmila Upadhyaya, Benjamin Zapilko, Harald Sack, Stefan Dietze

TL;DR
This paper discusses the development of Research Knowledge Graphs within NFDI4DataScience to enhance transparency, reproducibility, and discoverability in AI and Data Science research through semantic technologies and community efforts.
Contribution
It presents recent advances in creating semantically rich RKGs using ontologies, standards, and automation, along with tools and community projects supporting FAIR principles.
Findings
Development of the NFDI4DS ontology
Implementation of metadata standards and tools
Community-led projects and RKG applications
Abstract
As research in Artificial Intelligence and Data Science continues to grow in volume and complexity, it becomes increasingly difficult to ensure transparency, reproducibility, and discoverability. To address these challenges, as research artifacts should be understandable and usable by machines, the NFDI4DataScience consortium is developing and providing Research Knowledge Graphs (RKGs). Building upon earlier works, this paper presents recent progress in creating semantically rich RKGs using standardized ontologies, shared vocabularies, and automated Information Extraction techniques. Key achievements include the development of the NFDI4DS ontology, metadata standards, tools, and services designed to support the FAIR principles, as well as community-led projects and various implementations of RKGs. Together, these efforts aim to capture and connect the complex relationships between…
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Taxonomy
TopicsResearch Data Management Practices · Advanced Graph Neural Networks · Semantic Web and Ontologies
