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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
12307
Venue
VLDB
Year
2020
Pagerank
6.9344762e-05
Overall Rank
4,056 | 72.18%
DOI
10.14778/3407790.3407828

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{sun_vldb20,
        title = {{A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs}},
        author = {Sun, Zequn and Zhang, Qingheng and Hu, Wei and Wang, Chengming and Chen, Muhao and Akrami, Farahnaz and Li, Chengkai},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2326--2340},
        doi = {10.14778/3407790.3407828},
        url = {https://doi.org/10.14778/3407790.3407828},
        year = {2020}
}

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