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Efficient Knowledge Graph Accuracy Evaluation

Summary: Efficient KG accuracy evaluation: statistical guarantees; cluster sampling reduces annotation costs. Weighted, two-stage, and stratified sampling enable incremental evaluation on evolving KGs (reservoir variant), yielding 60–80% cost reduction with preserved accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11856
Venue
VLDB
Year
2019
Pagerank
4.9623586e-05
Overall Rank
6,689 | 53.47%
DOI
10.14778/3342263.3342642

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
10,476 Credible Intervals for Knowledge Graph Accuracy Estimation 2025 SIGMOD 4.1945683e-05
10,991 Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability 2024 SIGMOD 4.1945683e-05
11,029 Efficient and Reliable Estimation of Knowledge Graph Accuracy 2024 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
14 Online Aggregation 1997 SIGMOD 0.0010801504
1,627 Data Cleaning: Overview and Emerging Challenges 2016 SIGMOD 0.00011086905
2,184 A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data 2014 SIGMOD 9.3429789e-05
5,896 In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling 2017 VLDB 5.2847867e-05
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