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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 34

COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment

February 1, 2023

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Abstract:

Entity alignment is a fundamental and vital task in Knowledge Graph (KG) construction and fusion. Previous works mainly focus on capturing the structural semantics of entities by learning the entity embeddings on the relational triples and pre-aligned "seed entities". Some works also seek to incorporate the attribute information to assist refining the entity embeddings. However, there are still many problems not considered, which dramatically limits the utilization of attribute information in the entity alignment. Different KGs may have lots of different attribute types, and even the same attribute may have diverse data structures and value granularities. Most importantly, attributes may have various "contributions" to the entity alignment. To solve these problems, we propose COTSAE that combines the structure and attribute information of entities by co-training two embedding learning components, respectively. We also propose a joint attention method in our model to learn the attentions of attribute types and values cooperatively. We verified our COTSAE on several datasets from real-world KGs, and the results showed that it is significantly better than the latest entity alignment methods. The structure and attribute information can complement each other and both contribute to performance improvement.

Published Date: 2020-06-02

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved

Authors

Kai Yang

Peking University


Shaoqin Liu

Peking University


Junfeng Zhao

Peking University


Yasha Wang

Peking University


Bing Xie

Peking University


DOI:

10.1609/aaai.v34i03.5696


Topics: AAAI

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HOW TO CITE:

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment Proceedings of the AAAI Conference on Artificial Intelligence, 34 (2020) 3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment AAAI 2020, 3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie (2020). COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie. COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 34 2020 p.3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie. 2020. COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. "Proceedings of the AAAI Conference on Artificial Intelligence, 34". 3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie. (2020) "COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment", Proceedings of the AAAI Conference on Artificial Intelligence, 34, p.3025-3032

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie, "COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment", AAAI, p.3025-3032, 2020.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie. "COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment". Proceedings of the AAAI Conference on Artificial Intelligence, 34, 2020, p.3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie. "COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment". Proceedings of the AAAI Conference on Artificial Intelligence, 34, (2020): 3025-3032.

Kai Yang||Shaoqin Liu||Junfeng Zhao||Yasha Wang||Bing Xie. COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. AAAI[Internet]. 2020[cited 2023]; 3025-3032.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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