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RePS: Relation, Position and Structure aware Entity Alignment

Authors:

Anil Surisetty,

Deepak Chaurasiya,

Aakarsh Malhotra,

Vikrant DeyAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 1083 - 1091

https://doi.org/10.1145/3487553.3524720

Published: 16 August 2022 Publication History

Abstract

Entity Alignment (EA) is the task of recognizing the same entity present in different knowledge bases. Recently, embedding-based EA techniques have established dominance where alignment is done based on closeness in latent space. Graph Neural Networks (GNN) gained popularity as the embedding module due to its ability to learn entities’ representation based on their local sub-graph structures. Although GNN shows promising results, limited works have aimed to capture relations while considering their global importance and entities’ relative position during EA. This paper presents Relation, Position and Structure aware Entity Alignment (RePS), a multi-faceted representation learning-based EA method that encodes local, global, and relation information for aligning entities. To capture relations and neighborhood structure, we propose a relation-based aggregation technique – Graph Relation Network (GRN) that incorporates relation importance during aggregation. To capture the position of an entity, we propose Relation-aware Position Aggregator (RPA) to capitalize entities’ position in a non-Euclidean space using training labels as anchors, which provides a global view of entities. Finally, we introduce a Knowledge Aware Negative Sampling (KANS) that generates harder to distinguish negative samples for the model to learn optimal representations. We perform exhaustive experimentation on four cross-lingual datasets and report an ablation study to demonstrate the effectiveness of GRN, KANS, and position encodings.

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Cited By

Tang WWang YGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Multi-modal Entity Alignment via Position-enhanced Multi-label PropagationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658085(366-375)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658085
Zeng WZhao XTang JFan C(2024)Knowledge Graph Alignment Under Scarce Supervision: A General Framework With Active Cross-View Contrastive LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332190035:9(11692-11705)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3321900
Gao YZhang LXu FIshdorj TSu Y(2024)An Entity Alignment Model for Echinococcosis Knowledge GraphAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5678-0_6(62-74)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5678-0_6
Show More Cited By

Index Terms

RePS: Relation, Position and Structure aware Entity Alignment
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications
    1. Data mining

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Cross-lingual knowledge graph entity alignment based on relation awareness and attribute involvement
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Entity alignment is an effective means of matching entities from various knowledge graphs (KGs) that represent the equivalent real-world object. With the development of representation learning, recent entity alignment methods learn entity ...
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Entity alignment, which aims to identify equivalent entities from various Knowledge Graphs (KGs), is a fundamental and critical task in knowledge graph fusion. Current entity alignment methods usually use relationship triples to represent the ...

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Published In

cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

Copyright © 2022 ACM.

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Publication History

Published: 16 August 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Tang WWang YGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Multi-modal Entity Alignment via Position-enhanced Multi-label PropagationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658085(366-375)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658085
Zeng WZhao XTang JFan C(2024)Knowledge Graph Alignment Under Scarce Supervision: A General Framework With Active Cross-View Contrastive LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332190035:9(11692-11705)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3321900
Gao YZhang LXu FIshdorj TSu Y(2024)An Entity Alignment Model for Echinococcosis Knowledge GraphAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5678-0_6(62-74)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5678-0_6
Tang WSu FSun HQi QWang JTao SYang HChua TLauw HSi LTerzi ETsaparas P(2023)Weakly Supervised Entity Alignment with Positional InspirationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570394(814-822)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570394
Zeng WZhao XTan ZTang JCheng X(2023)Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327258435:12(12770-12784)Online publication date: 3-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3272584
Yang JYang LWang HGao Y(2023)Multirelational Tensor Graph Attention Networks for Knowledge Fusion in Smart Enterprise SystemsIEEE Transactions on Industrial Informatics10.1109/TII.2022.319054819:1(616-625)Online publication date: Jan-2023
https://doi.org/10.1109/TII.2022.3190548
Surisetty AMalhotra AChaurasiya DModak SYerramsetty SSingh ASahir LAbdel-Raheem E(2023)Study of Topology Bias in GNN-based Knowledge Graphs Algorithms2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00150(1149-1156)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00150
Liu WPan JZhang XGong XYe YZhao XWang XWu KXiang HYan HZhang Q(2023)Cross-platform product matching based on entity alignment of knowledge graph with raea modelWorld Wide Web10.1007/s11280-022-01134-y26:4(2215-2235)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1007/s11280-022-01134-y
Zhao XZeng WTang JZhao XZeng WTang J(2023)Recent Advance of Representation Learning StageEntity Alignment10.1007/978-981-99-4250-3_3(51-75)Online publication date: 26-Oct-2023
https://doi.org/10.1007/978-981-99-4250-3_3

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