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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Dependent Learning of Entity Vectors for Entity Alignment on Knowledge Graphs |
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| 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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