PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models

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.

Machine Learning (cs.LG)

arXiv:2606.08926 [cs.LG]

(or arXiv:2606.08926v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.08926 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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