NFDI通用的、可重用的知识图谱基础设施案例

Lozana Rossenova, Moritz Schubotz, Renat Shigapov
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引用次数: 1

摘要

欧盟委员会的战略研究与创新议程(SRIA)将知识图(KGs)确定为构建互操作性框架和实现跨国家、部门和学科用户之间数据交换的最重要技术之一[1]。KG是一个图结构知识库,包含术语(词汇或本体)和通过术语相互关联的数据实体[2]。KGs基于语义web技术(RDF、SPARQL等),通常用于敏捷数据集成。KGs在德国也扮演着重要的角色,作为连接研究数据和研究相关实体并使其可访问的工具-例如GESIS知识图谱基础设施,TIB开放研究知识图谱和GND.network。此外,由德国维基媒体维护的Wikidata知识图谱包含了大量与研究相关的实体,除了是开放数据的重要倡导工具外,还广泛应用于科学知识管理[3]。使用Wikidata KG中的多学科、众包知识扩展特定领域的本体支持的KG将实现重要的应用。专家知识系统和世界知识之间的联系使普通人能够从高质量的研究数据中受益,并最终有助于增加社会对科学研究的信心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI
The Strategic Research and Innovation Agenda (SRIA) of the European Commission identifies Knowledge Graphs (KGs) as one of the most important technologies for building an interoperability framework and enabling data exchange among users across countries, sectors, and disciplines [1]. KG is a graph-structured knowledge base containing a terminology (vocabulary or ontology) and data entities interrelated via the terminology [2]. KGs are based on semantic web technologies (RDF, SPARQL, etc.) and often used for agile data integration. KGs also play an essential role within Germany as a vehicle to connect research data and research-related entities and make those accessible – examples include the GESIS Knowledge Graph Infrastructure, TIB Open Research Knowledge Graph, and GND.network. Furthermore, the Wikidata knowledge graph, maintained by Wikimedia Germany, contains a large number of research-related entities and is widely used in scientific knowledge management in addition to being an important advocacy tool for open data [3]. Extending domain-specific ontology-supported KGs with the multidisciplinary, crowdsourced knowledge in Wikidata KG would enable significant applications. The linking between expert knowledge systems and world knowledge empowers lay persons to benefit from high-quality research data and ultimately contributes to increasing confidence in scientific research in society.
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