构建可机器理解的研究知识图谱，提升数据科学成果的可发现性与可复现性。
Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions
- 基于标准本体与自动信息抽取，构建语义丰富的研究知识图谱。
- 开发了NFDI4DS本体与元数据规范，支持科研成果的开放共享。
- 适合关注科研可复现性与数据科学基础设施的研究者。
随着人工智能与数据科学研究的规模与复杂性持续增长，确保研究的透明性、可复现性和可发现性日益困难。为应对这一挑战，NFDI4DataScience联盟致力于构建研究知识图谱（RKG），使研究成果具备机器可读性。本文总结了在标准化本体、共享词汇表和自动化信息提取技术基础上，构建语义丰富RKG的最新进展。关键成果包括NFDI4DS本体、元数据标准、支持FAIR原则的工具与服务，以及由社区主导的多个RKG实施项目。这些工作旨在捕捉数据集、模型、软件与科学出版物之间的复杂关联，推动科研生态的互联与可持续发展。
原文摘要 · Abstract (English)
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.
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