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Credible Intervals for Knowledge Graph Accuracy Estimation

Summary: Proposes Credible Intervals (Bayesian) for KG accuracy instead of traditional Confidence Intervals. Introduces adaptive aHPD sampling for large real-world KGs, delivering stronger post-data reliability guarantees. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h5ad9340489bde830
Venue
SIGMOD
Year
2025
Pagerank
5.1708619e-05
Overall Rank
9,479 | 36.27%
DOI
10.1145/3725279

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{marchesin_sigmod25,
        title = {{Credible Intervals for Knowledge Graph Accuracy Estimation}},
        author = {Marchesin, Stefano and Silvello, Gianmaria},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725279},
        url = {https://dl.acm.org/doi/10.1145/3725279},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,765 LLMs as Stratification Signals for KG Accuracy Evaluation 2026 VLDB 4.9793485e-05
10,815 CRAFT: Corpus Relatedness Analysis Using Fourier Transforms 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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