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2016 | OriginalPaper | Chapter

A Joint Embedding Method for Entity Alignment of Knowledge Bases

Authors : Yanchao Hao, Yuanzhe Zhang, Shizhu He, Kang Liu, Jun Zhao

Published in: Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data

Publisher: Springer Singapore

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Abstract

We propose a model which jointly learns the embeddings of multiple knowledge bases (KBs) in a uniform vector space to align entities in KBs. Instead of using content similarity based methods, we think the structure information of KBs is also important for KB alignment. When facing the cross-linguistic or different encoding situation, what we can leverage are only the structure information of two KBs. We utilize seed entity alignments whose embeddings are ensured the same in the joint learning process. We perform experiments on two datasets including a subset of Freebase comprising 15 thousand selected entities, and a dataset we construct from real-world large scale KBs – Freebase and DBpedia. The results show that the proposed approach which only utilize the structure information of KBs also works well.

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next chapter A Multi-dimension Weighted Graph-Based Path Planning with Avoiding Hotspots
Footnotes
4
In step5, 7 and 35 are empirical values chosen in experiments.
 
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Metadata
Title
A Joint Embedding Method for Entity Alignment of Knowledge Bases
Authors
Yanchao Hao
Yuanzhe Zhang
Shizhu He
Kang Liu
Jun Zhao
Copyright Year
2016
Publisher
Springer Singapore
Book
Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data

Print ISBN: 978-981-10-3167-0

Electronic ISBN: 978-981-10-3168-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-3168-7_1

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