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dc.contributor.authorYang, Hsiu-Wei 20:28:50 (GMT) 20:28:50 (GMT)
dc.description.abstractMultilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity matching is to align entities in a source language with their counterparts in target languages. In this thesis, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other. Specifically, we apply graph convolutional networks (GCNs) to combine multi-aspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings. To exploit the literal descriptions of entities expressed in different languages, we propose two uses of a pre-trained multilingual BERT model to bridge cross-lingual gaps. We further propose two strategies to integrate GCN-based and BERT-based modules to boost performance. Extensive experiments on two benchmark datasets demonstrate that our method significantly outperforms existing systems. We additionally introduce a new dataset comprised of 15 low-resource languages and featured with unlinkable cases to draw closer to the real-world challenges.en
dc.publisherUniversity of Waterlooen
dc.subjectentity matchingen
dc.subjectknowledge graphen
dc.subjectentity alignmenten
dc.subjectgraph embeddingen
dc.titleCross-Lingual Entity Matching for Knowledge Graphsen
dc.typeMaster Thesisen
dc.pendingfalse R. Cheriton School of Computer Scienceen Scienceen of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorLin, Jimmy
uws.contributor.affiliation1Faculty of Mathematicsen

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