PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models
2026-06-08 • Machine Learning
Machine Learning
AI summaryⓘ
The authors created PROBE-Web, a tool that helps people test how well knowledge graph completion (KGC) models work from different angles. Instead of just using usual scores, their system lets users adjust settings to see how sharp and popular-bias resistant the models are. It has a simple interface to compare multiple models, understand their pros and cons, and explore detailed examples. This helps users pick or improve KGC models based on what matters most to them.
Knowledge Graph CompletionMRRHits@KEvaluation MetricsPredictive SharpnessPopularity BiasInteractive SystemModel ExplainabilityGraph Models
Authors
Sooho Moon, Yunyong Ko
Abstract
Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring different evaluation perspectives. In this demo, we present PROBE-Web, an interactive system for probing diverse evaluation landscapes for KGC models. PROBE-Web enables users to flexibly evaluate KGC models by adjusting two critical perspectives: (P1) predictive sharpness and (P2) popularity-bias robustness. Through a user-friendly GUI, users easily evaluate multiple KGC models and analyze their strengths and weaknesses. PROBE-Web provides four key functionalities: (1) conventional evaluation toolkit, (2) flexible perspective-aware evaluation, (3) explainable case studies, and (4) evaluation landscape exploration. We believe that PROBE-Web can help users better understand KGC models aligning with their objectives.