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Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. Predicting Missing Links Using PyKEEN
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Fulltext urn:nbn:de:0011-n-5649997 (303 KByte PDF) MD5 Fingerprint: 1b496b9c98a0ab78758325403177d86a Created on: 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/
pp.245-248 |
| International Semantic Web Conference (ISWC) <18, 2019, Auckland> |
| Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01IS18050F; MLWin |
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| English |
| Conference Paper, Electronic Publication |
| 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.