Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions

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 datasets, models, software, and scientific publications.