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A Critical Re-evaluation of Neural Methods for Entity Alignment

Summary: Comparative study of pre-neural and neural EA methods with standardized matching modules; connects EA to record linkage. Paris, a non-neural baseline, beats neural methods across datasets; recommends Paris as baseline and reframes neural positioning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12674
Venue
VLDB
Year
2022
Pagerank
4.5138915e-05
Overall Rank
8,436 | 41.32%
DOI
10.14778/3529337.3529355

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,908 Deep Active Alignment of Knowledge Graph Entities and Schemata 2023 SIGMOD 4.427232e-05
9,487 Making It Tractable to Catch Duplicates and Conflicts in Graphs 2023 SIGMOD 4.3341665e-05
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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