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Semantic knowledge graph embeddings for biomedical research: data integration using linked open data

: Dörpinghaus, J.; Jacobs, M.

Volltext ()

Alam, Mehwish (ed.):
SEMPDS 2019, Posters and Demos at SEMANTiCS 2019. Online resource : Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019) Karlsruhe, Germany, September 9th to 12th, 2019
Karlsruhe: CEUR, 2019 (CEUR Workshop Proceedings 2451)
ISSN: 1613-0073
5 S.
International Conference on Semantic Systems (SEMANTiCS) <15, 2019, Karlsruhe>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer SCAI ()

Knowledge Graphs are becoming a key instrument for biomedical
knowledge discovery and modeling. These approaches rely on structured
data, e.g. about related proteins or genes, and form cause-and-effect networks or - if enriched with literature data and other linked data
sources - knowledge graphs. A key aspect of analysis on these graphs is
the missing context. Here we present a novel semantic approach towards
a context enriched Knowledge Graph for biomedical research utilizing
data integration with linked data. The result is a general graph concept
that can be used for graph embeddings in different contexts or layers.