Efficient Knowledge Graph Accuracy Evaluation

Understand

Estimation of the accuracy of a large-scale knowledge graph (KG) often requires humans to annotate samples from the graph.

• How to obtain statistically meaningful estimates for accuracy evaluation while keeping human annotation costs low is a problem critical to the development cycle of a KG and its practical applications.
• Surprisingly, this challenging problem has largely been ignored in prior research.
• To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts.