Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions
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| Format: | Preprint |
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2025
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| author | Silva, Kanishka Ackermann, Marcel R. Fliegl, Heike Gesese, Genet-Asefa Limani, Fidan Mayr, Philipp Mutschke, Peter Oelen, Allard Suryani, Muhammad Asif Upadhyaya, Sharmila Zapilko, Benjamin Sack, Harald Dietze, Stefan |
| author_facet | Silva, Kanishka Ackermann, Marcel R. Fliegl, Heike Gesese, Genet-Asefa Limani, Fidan Mayr, Philipp Mutschke, Peter Oelen, Allard Suryani, Muhammad Asif Upadhyaya, Sharmila Zapilko, Benjamin Sack, Harald Dietze, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions Silva, Kanishka Ackermann, Marcel R. Fliegl, Heike Gesese, Genet-Asefa Limani, Fidan Mayr, Philipp Mutschke, Peter Oelen, Allard Suryani, Muhammad Asif Upadhyaya, Sharmila Zapilko, Benjamin Sack, Harald Dietze, Stefan Information Retrieval 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. |
| title | Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.02300 |