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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 4: AAAI-22 Technical Tracks 4

Ensemble Semi-supervised Entity Alignment via Cycle-Teaching

February 1, 2023

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Authors

Kexuan Xin

The University of Queensland


Zequn Sun

Nanjing University


Wen Hua

The University of Queensland


Bing Liu

The University of Queensland


Wei Hu

Nanjing University


Jianfeng Qu

Soochow University


Xiaofang Zhou

The Hong Kong University of Science and Technology


DOI:

10.1609/aaai.v36i4.20348


Abstract:

Entity alignment is to find identical entities in different knowledge graphs. Although embedding-based entity alignment has recently achieved remarkable progress, training data insufficiency remains a critical challenge. Conventional semi-supervised methods also suffer from the incorrect entity alignment in newly proposed training data. To resolve these issues, we design an iterative cycle-teaching framework for semi-supervised entity alignment. The key idea is to train multiple entity alignment models (called aligners) simultaneously and let each aligner iteratively teach its successor the proposed new entity alignment. We propose a diversity-aware alignment selection method to choose reliable entity alignment for each aligner. We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher. Besides, considering the influence of cycle-teaching order, we elaborately design a strategy to arrange the optimal order that can maximize the overall performance of multiple aligners. The cycle-teaching process can break the limitations of each model's learning capability and reduce the noise in new training data, leading to improved performance. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed cycle-teaching framework, which significantly outperforms the state-of-the-art models when the training data is insufficient and the new entity alignment has much noise.

Topics: AAAI

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HOW TO CITE:

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou Ensemble Semi-supervised Entity Alignment via Cycle-Teaching Proceedings of the AAAI Conference on Artificial Intelligence (2022) 4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou Ensemble Semi-supervised Entity Alignment via Cycle-Teaching AAAI 2022, 4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou (2022). Ensemble Semi-supervised Entity Alignment via Cycle-Teaching. Proceedings of the AAAI Conference on Artificial Intelligence, 4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou. Ensemble Semi-supervised Entity Alignment via Cycle-Teaching. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou. 2022. Ensemble Semi-supervised Entity Alignment via Cycle-Teaching. "Proceedings of the AAAI Conference on Artificial Intelligence". 4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou. (2022) "Ensemble Semi-supervised Entity Alignment via Cycle-Teaching", Proceedings of the AAAI Conference on Artificial Intelligence, p.4281-4289

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou, "Ensemble Semi-supervised Entity Alignment via Cycle-Teaching", AAAI, p.4281-4289, 2022.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou. "Ensemble Semi-supervised Entity Alignment via Cycle-Teaching". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou. "Ensemble Semi-supervised Entity Alignment via Cycle-Teaching". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 4281-4289.

Kexuan Xin||Zequn Sun||Wen Hua||Bing Liu||Wei Hu||Jianfeng Qu||Xiaofang Zhou. Ensemble Semi-supervised Entity Alignment via Cycle-Teaching. AAAI[Internet]. 2022[cited 2023]; 4281-4289.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
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