Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses
Researchers have introduced PROBE, a novel framework for evaluating knowledge graph completion (KGC) models, addressing limitations in existing metrics. PROBE accounts for predictive sharpness and popularity-bias robustness, properties often overlooked. A companion system, PROBE-Web, offers an interactive interface for users to explore these evaluation landscapes and compare KGC models. AI
IMPACT Enhances evaluation of knowledge graph completion models, potentially leading to more reliable applications in areas like drug discovery and RAG.