交互式工具，可灵活评估知识图谱补全模型的预测精度与偏见鲁棒性。
PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models
- 通过调整预测锐度和流行度偏见鲁棒性两个维度，实现多视角评估
- 支持多种模型对比分析，揭示不同模型在不同评价标准下的表现差异
- 适合研究者和开发者用于深入理解模型特性，优化实际应用
知识图谱补全（KGC）模型通常使用基于排名的指标（如MRR和Hits@K）进行评估，但不同用户可能需要不同的评估视角。本文演示了PROBE-Web——一个用于探测KGC模型评估景观的交互式系统。该系统允许用户灵活调整两个关键评估维度：(P1) 预测锐度，(P2) 流行度偏见鲁棒性。通过友好的图形界面，用户可轻松评估多个KGC模型并分析其优缺点。PROBE-Web提供四大核心功能：(1) 传统评估工具包，(2) 可配置视角的评估，(3) 可解释的案例分析，(4) 评估景观探索。我们相信，PROBE-Web能帮助用户更贴合自身目标地理解KGC模型。
原文摘要 · Abstract (English)
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.
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