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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
6324
Venue
SIGMOD
Year
2022
Pagerank
5.8892326e-05
Overall Rank
4,837 | 66.36%
DOI
10.1145/3514221.3517872

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