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Entity Resolution with Hierarchical Graph Attention Networks

Summary: Proposes HierGAT, a hierarchical graph-attention ER model that jointly reasons about interdependent decisions, not pairwise. Uses graph attention to identify discriminative attributes/words, achieving up to 32.5% F1 over DeepMatcher and 8.7% over Ditto. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6386
Venue
SIGMOD
Year
2022
Pagerank
6.6150054e-05
Overall Rank
4,590 | 68.51%
DOI
10.1145/3514221.3517872

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yao_sigmod22,
        title = {{Entity Resolution with Hierarchical Graph Attention Networks}},
        author = {Yao, Dezhong and Gu, Yuhong and Cong, Gao and Jin, Hai and Lv, Xinqiao},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517872},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517872},
        year = {2022}
}

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