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Towards the semantic formalization of science

: Fathalla, S.; Auer, S.; Lange, C.


Hung, C.-C. ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Applied Computing -SIGAPP-:
SAC 2020, 35th ACM/SIGAPP Symposium on Applied Computing. Proceedings : Brno, Czech Republic, March 30 - April 3, 2020
New York: ACM, 2020
ISBN: 978-1-4503-6866-7
Symposium on Applied Computing (SAC) <35, 2020, Brno>
Conference Paper
Fraunhofer FIT ()

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.