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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Dependent Learning of Entity Vectors for Entity Alignment on Knowledge Graphs
Organization Unit
  • Leon Ruppen
  • Abraham Bernstein
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Date 2018
Abstract Text The linking of correspondent entities between multiple knowledge graphs (KGs) is known as entity alignment. This thesis introduces the embedding-based method Dependent Learning of Entity Vectors (DELV) for entity alignment. In an iterative fashion, the method learns a low-dimensional vector representation for the entities in a satellite model in dependence of a pretrained central model. Word2vec and rdf2vec constitute the basis for the embedding learning process. DELV is evaluated on real-world datasets, originating from the three knowledge graphs DBpedia, Wikidata and Freebase. DELV outperforms most of its baselines in terms of the mean rank, the hits@1 and hits@10. While entity alignment is normally performed on two KGs, this thesis also demonstrates how DELV can be efficiently used for alignment of unlimited KGs.
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