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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.0006429466 |
| 2,320 | High-Throughput Vector Similarity Search in Knowledge Graphs | 2023 | SIGMOD | 9.0366225e-05 |
| 2,847 | Building, Maintaining, and Using Knowledge Bases: A Report from the Trenches | 2013 | SIGMOD | 8.0224023e-05 |
| 3,711 | Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale | 2022 | SIGMOD | 6.823609e-05 |
| 5,538 | Growing and Serving Large Open-domain Knowledge Graphs | 2023 | SIGMOD | 5.4509524e-05 |
| 6,689 | Efficient Knowledge Graph Accuracy Evaluation | 2019 | VLDB | 4.9623586e-05 |
| 11,029 | Efficient and Reliable Estimation of Knowledge Graph Accuracy | 2024 | VLDB | 4.1945683e-05 |
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