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search.filters.applied.f.access: openAccess × Has files: Yes × Subject: Knowledge Graphs × WGL Institute: TIB ×

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    Towards the semantic formalization of science
    (New York City, NY : Association for Computing Machinery, 2020) Fathalla, Said; Auer, Sören; Lange, Christoph
    The past decades have witnessed a huge growth in scholarly information published on the Web, mostly in unstructured or semi-structured formats, which hampers scientific literature exploration and scientometric studies. Past studies on ontologies for structuring scholarly information focused on describing scholarly articles' components, such as document structure, metadata and bibliographies, rather than the scientific work itself. Over the past four years, we have been developing the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modeling the research findings in various fields of modern science resulting in a knowledge graph. Here, we introduce this ontology suite and discuss the design considerations taken into account during its development. We deem that within the next years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work.
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    The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI
    (Hannover : TIB Open Publishing, 2023) Rossenova, Lozana; Schubotz, Moritz; Shigapov, Renat
    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.