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

arXiv 2606.08926 ↗First posted 8 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 8 Jun 2026.

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References · 12

  1. 2016Complex Embeddings for Simple Link Predictioncited by 902
  2. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  3. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  4. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  5. 2018DKN: Deep Knowledge-Aware Network for News Recommendationcited by 137
  6. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  7. 2020RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphscited by 118
  8. 2021Knowledge Graph Reasoning with Relational Digraphcited by 92
  9. 2019A Re-evaluation of Knowledge Graph Completion Methodscited by 73
  10. 2019Probabilistic Logic Neural Networks for Reasoningcited by 69
  11. 2020Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learningcited by 40
  12. 2020Drug Repurposing for COVID-19 via Knowledge Graph Completioncited by 30

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