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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 5: AAAI-21 Technical Tracks 5

Visual Pivoting for (Unsupervised) Entity Alignment

February 1, 2023

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Authors

Fangyu Liu

University of Cambridge


Muhao Chen

University of Southern California, University of Pennsylvania


Dan Roth

University of Pennsylvania


Nigel Collier

University of Cambridge


DOI:

10.1609/aaai.v35i5.16550


Abstract:

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an initial seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences. Code release: https://github.com/cambridgeltl/eva; project page: http://cogcomp.org/page/publication view/927.

Topics: AAAI

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HOW TO CITE:

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier Visual Pivoting for (Unsupervised) Entity Alignment Proceedings of the AAAI Conference on Artificial Intelligence (2021) 4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier Visual Pivoting for (Unsupervised) Entity Alignment AAAI 2021, 4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier (2021). Visual Pivoting for (Unsupervised) Entity Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier. Visual Pivoting for (Unsupervised) Entity Alignment. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier. 2021. Visual Pivoting for (Unsupervised) Entity Alignment. "Proceedings of the AAAI Conference on Artificial Intelligence". 4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier. (2021) "Visual Pivoting for (Unsupervised) Entity Alignment", Proceedings of the AAAI Conference on Artificial Intelligence, p.4257-4266

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier, "Visual Pivoting for (Unsupervised) Entity Alignment", AAAI, p.4257-4266, 2021.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier. "Visual Pivoting for (Unsupervised) Entity Alignment". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier. "Visual Pivoting for (Unsupervised) Entity Alignment". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 4257-4266.

Fangyu Liu||Muhao Chen||Dan Roth||Nigel Collier. Visual Pivoting for (Unsupervised) Entity Alignment. AAAI[Internet]. 2021[cited 2023]; 4257-4266.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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