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Efficient and Reliable Estimation of Knowledge Graph Accuracy

Summary: Replaces Wald-based confidence intervals for KG accuracy estimation with Wilson-based intervals adapted to complex sampling designs, eliminating zero-width and overshooting issues. Delivers up to 2× reliability improvement while preserving or improving sampling efficiency across KG sizes/topologies. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
hf1f3f8b229f7d708
Venue
VLDB
Year
2024
Pagerank
4.9793485e-05
Overall Rank
11,572 | 22.20%
DOI
10.14778/3665844.3665865

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Authors

BibTeX Citation

@article{marchesin_vldb24,
        title = {{Efficient and Reliable Estimation of Knowledge Graph Accuracy}},
        author = {Marchesin, Stefano and Silvello, Gianmaria},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {9},
        pages = {2392--2404},
        doi = {10.14778/3665844.3665865},
        url = {https://doi.org/10.14778/3665844.3665865},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,479 Credible Intervals for Knowledge Graph Accuracy Estimation 2025 SIGMOD 5.1708619e-05
10,765 LLMs as Stratification Signals for KG Accuracy Evaluation 2026 VLDB 4.9793485e-05
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