Details
| Original language | English |
|---|---|
| Title of host publication | SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 2057-2059 |
| Number of pages | 3 |
| ISBN (electronic) | 9781450368667 |
| Publication status | Published - 30 Mar 2020 |
| Event | 35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic Duration: 30 Mar 2020 → 3 Apr 2020 |
Publication series
| Name | Proceedings of the ACM Symposium on Applied Computing |
|---|
Abstract
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.
Keywords
- Knowledge capture, Knowledge graphs, Scholarly communication, Semantic metadata enrichment
ASJC Scopus subject areas
- Computer Science(all)
- Software
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SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing. New York: Association for Computing Machinery (ACM), 2020. p. 2057-2059 (Proceedings of the ACM Symposium on Applied Computing).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards the semantic formalization of science
AU - Fathalla, Said
AU - Auer, Sören
AU - Lange, Christoph
N1 - Funding Information: This work has been supported by ERC project ScienceGRAPH no. 819536.
PY - 2020/3/30
Y1 - 2020/3/30
N2 - 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.
AB - 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.
KW - Knowledge capture
KW - Knowledge graphs
KW - Scholarly communication
KW - Semantic metadata enrichment
UR - http://www.scopus.com/inward/record.url?scp=85083033619&partnerID=8YFLogxK
U2 - 10.1145/3341105.3374132
DO - 10.1145/3341105.3374132
M3 - Conference contribution
AN - SCOPUS:85083033619
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 2057
EP - 2059
BT - SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing
PB - Association for Computing Machinery (ACM)
CY - New York
T2 - 35th Annual ACM Symposium on Applied Computing, SAC 2020
Y2 - 30 March 2020 through 3 April 2020
ER -