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A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs

Summary: Comprehensive benchmarking study of embedding-based entity alignment for knowledge graphs, surveying 23 approaches and classifying techniques. Proposes a KG sampling algorithm to generate diverse benchmarks and releases an open-source library of 12 methods with extensive experiments, highlighting strengths and gaps. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12119
Venue
VLDB
Year
2020
Pagerank
6.6332294e-05
Overall Rank
3,915 | 72.77%
DOI
10.14778/3407790.3407828

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