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)
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Authors
- 1. Stefano Marchesin (University of Padua)
- 2. Gianmaria Silvello (University of Padua)
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}
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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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.00038697603 |
| 1,631 | High-Throughput Vector Similarity Search in Knowledge Graphs | 2023 | SIGMOD | 0.00010174628 |
| 2,647 | Building, Maintaining, and Using Knowledge Bases: A Report from the Trenches | 2013 | SIGMOD | 8.2959636e-05 |
| 3,304 | Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale | 2022 | SIGMOD | 7.5404346e-05 |
| 5,652 | Growing and Serving Large Open-domain Knowledge Graphs | 2023 | SIGMOD | 6.1335866e-05 |
| 6,581 | Efficient Knowledge Graph Accuracy Evaluation | 2019 | VLDB | 5.8364579e-05 |
| 11,239 | Efficient and Reliable Estimation of Knowledge Graph Accuracy | 2024 | VLDB | 5.093636e-05 |
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