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Efficient Knowledge Graph Accuracy Evaluation

Summary: Efficient KG accuracy evaluation: statistical guarantees; cluster sampling reduces annotation costs. Weighted, two-stage, and stratified sampling enable incremental evaluation on evolving KGs (reservoir variant), yielding 60–80% cost reduction with preserved accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h7ac2be7b3f2bee38
Venue
VLDB
Year
2019
Pagerank
6.1558829e-05
Overall Rank
5,379 | 63.84%
DOI
10.14778/3342263.3342642

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{gao_vldb19,
        title = {{Efficient Knowledge Graph Accuracy Evaluation}},
        author = {Gao, Junyang and Li, Xian and Xu, Yifan Ethan and Sisman, Bunyamin and Dong, Xin Luna and Yang, Jun},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {11},
        pages = {1679--1691},
        doi = {10.14778/3342263.3342642},
        url = {https://doi.org/10.14778/3342263.3342642},
        year = {2019}
}

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