research-article

Deep Active Alignment of Knowledge Graph Entities and Schemata

Authors:

Jiacheng Huang,

Wei HuAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 159, Pages 1 - 26

https://doi.org/10.1145/3589304

Published: 20 June 2023 Publication History

Abstract

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.

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Cao ZLi JWang ZLi JBaeza-Yates RBonchi F(2024)DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671997(222-230)Online publication date: 25-Aug-2024
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Index Terms

Deep Active Alignment of Knowledge Graph Entities and Schemata
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
  2. Machine learning
    1. Learning settings
      1. Active learning settings
2. Information systems
  1. Data management systems
    1. Information integration
      1. Entity resolution

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

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Divyakant Agrawal
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Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Cited By

Bača R(2024)Window Function Expression: Let the Self-Join EnterProceedings of the VLDB Endowment10.14778/3665844.366584817:9(2162-2174)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3665844.3665848
Yan YWong R(2024)Proximity Queries on Point Clouds using Rapid Construction Path OracleProceedings of the ACM on Management of Data10.1145/36392612:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639261
Cao ZLi JWang ZLi JBaeza-Yates RBonchi F(2024)DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671997(222-230)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671997
Egger MMa WMottin DKarras PBordino IGullo FAnagnostopoulos AChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ReliK: A Reliability Measure for Knowledge Graph EmbeddingsProceedings of the ACM Web Conference 202410.1145/3589334.3645430(2009-2019)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645430
Xu BLu YSu BYan X(2024)Position-Aware Active Learning for Multi-Modal Entity AlignmentICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447624(8215-8219)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447624
Bergami GAppleby SMorgan G(2023)Specification Mining over Temporal DataComputers10.3390/computers1209018512:9(185)Online publication date: 14-Sep-2023
https://doi.org/10.3390/computers12090185

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