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  4. Predicting Missing Links Using PyKEEN
 
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2019
Conference Paper
Titel

Predicting Missing Links Using PyKEEN

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.
Author(s)
Ali, Mehdi
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hoyt, Charles Tapley
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Domingo-Fernandez, Daniel
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Lehmann, Jens
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hauptwerk
ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas). Proceedings. Online resource
Project(s)
MLWin
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Semantic Web Conference (ISWC) 2019
File(s)
N-564999.pdf (303.49 KB)
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Tags
  • Knowledge Graphs

  • bioinformatic

  • network representatio...

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