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  4. Predicting Missing Links Using PyKEEN
 
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2019
Conference Paper
Title

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  
Mainwork
ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas). Proceedings. Online resource  
Project(s)
MLWin
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
International Semantic Web Conference (ISWC) 2019  
Open Access
File(s)
Download (303.49 KB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.24406/publica-fhg-405535
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • Knowledge Graphs

  • bioinformatic

  • network representation learning

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