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Predicting Missing Links Using PyKEEN

 
: Ali, Mehdi; Hoyt, Charles Tapley; Domingo-Fernandez, Daniel; Lehmann, Jens

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Volltext urn:nbn:de:0011-n-5649997 (303 KByte PDF)
MD5 Fingerprint: 1b496b9c98a0ab78758325403177d86a
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Erstellt am: 12.11.2019


Suárez-Figueroa, M.C.:
ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas). Proceedings. Online resource : Co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 26-30, 2019
Auckland, 2019 (CEUR Workshop Proceedings 2456)
http://ceur-ws.org/Vol-2456/
S.245-248
International Semantic Web Conference (ISWC) <18, 2019, Auckland>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer SCAI ()
Fraunhofer IAIS ()
Knowledge Graphs; bioinformatic; network representation learning

Abstract
PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert.

: http://publica.fraunhofer.de/dokumente/N-564999.html