LLMs as Stratification Signals for KG Accuracy Evaluation

Summary: Uses aggregated LLM predictions as stratification signals—not truth estimators—for statistically guaranteed, lower-cost KG accuracy sampling. Distillation transfers these signals to student models, cutting annotation to 0.25% of facts and reducing costs 11–54% across six large KGs. (summarized by gpt-5.6-luna on Aug 17 2026)

Paper ID: hcf1e4f9d4c3f2e60
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,765 | 27.63%
DOI: 10.14778/3819518.3819530

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Stefano Marchesin (University of Padua)
2. Matteo Ceccarello (University of Padua)
3. Gianmaria Silvello (University of Padua)

BibTeX Citation

@article{marchesin_vldb26,
        title = {{LLMs as Stratification Signals for KG Accuracy Evaluation}},
        author = {Marchesin, Stefano and Ceccarello, Matteo and Silvello, Gianmaria},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {9},
        pages = {2005--2018},
        doi = {10.14778/3819518.3819530},
        url = {https://doi.org/10.14778/3819518.3819530},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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.

Rank	Cited Paper	Year	Venue	Pagerank
1,373	High-Throughput Vector Similarity Search in Knowledge Graphs	2023	SIGMOD	0.0001088854
2,428	Building, Maintaining, and Using Knowledge Bases: A Report from the Trenches	2013	SIGMOD	8.4815756e-05
3,013	Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale	2022	SIGMOD	7.7541678e-05
4,903	Growing and Serving Large Open-domain Knowledge Graphs	2023	SIGMOD	6.3636028e-05
5,379	Efficient Knowledge Graph Accuracy Evaluation	2019	VLDB	6.1558829e-05
9,479	Credible Intervals for Knowledge Graph Accuracy Estimation	2025	SIGMOD	5.1708619e-05
11,572	Efficient and Reliable Estimation of Knowledge Graph Accuracy	2024	VLDB	4.9793485e-05

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