research-article

Collective Multi-type Entity Alignment Between Knowledge Graphs

Authors:

Bunyamin Sisman,

Christos Faloutsos,

Jiawei HanAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2241 - 2252

https://doi.org/10.1145/3366423.3380289

Published: 20 April 2020 Publication History

Abstract

Knowledge graph (e.g. Freebase, YAGO) is a multi-relational graph representing rich factual information among entities of various types. Entity alignment is the key step towards knowledge graph integration from multiple sources. It aims to identify entities across different knowledge graphs that refer to the same real world entity. However, current entity alignment systems overlook the sparsity of different knowledge graphs and can not align multi-type entities by one single model. In this paper, we present a Collective Graph neural network for Multi-type entity Alignment, called CG-MuAlign. Different from previous work, CG-MuAlign jointly aligns multiple types of entities, collectively leverages the neighborhood information and generalizes to unlabeled entity types. Specifically, we propose novel collective aggregation function tailored for this task, that (1) relieves the incompleteness of knowledge graphs via both cross-graph and self attentions, (2) scales up efficiently with mini-batch training paradigm and effective neighborhood sampling strategy. We conduct experiments on real world knowledge graphs with millions of entities and observe the superior performance beyond existing methods. In addition, the running time of our approach is much less than the current state-of-the-art deep learning methods.

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https://doi.org/10.3390/app14093648
Zeng KJin HLv XZhu FHou LZhang YPang FQi YLiu DLi JFeng L(2024)XLORE 3: A Large-Scale Multilingual Knowledge Graph from Heterogeneous Wiki Knowledge ResourcesACM Transactions on Information Systems10.1145/366052142:6(1-47)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660521
Wu DLi TZhao YLiu JTang ZYang Z(2024)A Novel Entity and Relation Joint Interaction Learning Approach for Entity AlignmentInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450004934:05(821-843)Online publication date: 19-Mar-2024
https://doi.org/10.1142/S0218194024500049
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Index Terms

Collective Multi-type Entity Alignment Between Knowledge Graphs
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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Cited By

Fang JYan X(2024)MDSEA: Knowledge Graph Entity Alignment Based on Multimodal Data SupervisionApplied Sciences10.3390/app1409364814:9(3648)Online publication date: 25-Apr-2024
https://doi.org/10.3390/app14093648
Zeng KJin HLv XZhu FHou LZhang YPang FQi YLiu DLi JFeng L(2024)XLORE 3: A Large-Scale Multilingual Knowledge Graph from Heterogeneous Wiki Knowledge ResourcesACM Transactions on Information Systems10.1145/366052142:6(1-47)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660521
Wu DLi TZhao YLiu JTang ZYang Z(2024)A Novel Entity and Relation Joint Interaction Learning Approach for Entity AlignmentInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450004934:05(821-843)Online publication date: 19-Mar-2024
https://doi.org/10.1142/S0218194024500049
Vaganov DShikov ELysenko AAndreeva P(2024)Ontological model identification based on data from heterogeneous sourcesProcedia Computer Science10.1016/j.procs.2023.12.032229:C(305-314)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.12.032
Zhu BWang RWang JShao FWang K(2024)A survey: knowledge graph entity alignment research based on graph embeddingArtificial Intelligence Review10.1007/s10462-024-10866-457:9Online publication date: 3-Aug-2024
https://doi.org/10.1007/s10462-024-10866-4
Li AChen SLi ZQu JYue ZLiu J(2024)A Hierarchy-aware Entity Alignment Method for Educational Knowledge GraphsDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_21(324-341)Online publication date: 27-Oct-2024
https://doi.org/10.1007/978-981-97-5562-2_21
Sun ZHuang JXu XChen QRen WHu WKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)What makes entities similar? a similarity flooding perspective for multi-sourced knowledge graph embeddingsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619773(32875-32885)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619773
Qin YXiao YLiao XMeng WJensen CCremers CKirda E(2023)Vulnerability Intelligence Alignment via Masked Graph Attention NetworksProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3616686(2202-2216)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3576915.3616686
Yang LWang XZhang JYang JXu YHou JXin KWang F(2023)HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network AlignmentIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.314435010:2(746-759)Online publication date: Apr-2023
https://doi.org/10.1109/TCSS.2022.3144350
Vijayakumar SLouis F(2023)Revolutionizing Staffing and Recruiting with Contextual Knowledge Graphs and QNLP: An End-to-End Quantum Training Paradigm2023 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICKG59574.2023.00011(45-51)Online publication date: 1-Dec-2023
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