Database Paper Browser

Back to papers

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
11857
Venue
VLDB
Year
2019
Pagerank
4.9575975e-05
Overall Rank
6,690 | 53.51%
DOI
10.14778/3342263.3342642

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
10,486 Credible Intervals for Knowledge Graph Accuracy Estimation 2025 SIGMOD 4.1905499e-05
10,994 Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability 2024 SIGMOD 4.1905499e-05
11,032 Efficient and Reliable Estimation of Knowledge Graph Accuracy 2024 VLDB 4.1905499e-05
Previous Page 1 / 1 Next

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.

Previous Page 1 / 1 Next

Semantically Similar Papers