AI 中文总结
本文提出MissDiag框架，通过结构化缺失干预分解鲁棒性变化来源，发现不完全知识鲁棒性是类型化劣化现象，为KGQA与KG-RAG系统评估提供可解释基础。
AI 中文摘要
知识图谱问答（KGQA）与基于知识图谱的检索增强生成（KG-RAG）旨在将答案建立在显式图证据之上，但现实世界的知识图谱往往存在稀疏、过时和不完整的问题。现有鲁棒性评估通常报告证据被移除或扰动后答案质量的总体变化，这衡量了系统对不完全支持的敏感性，但未明确劣化的来源：相同的分数变化可能混淆缺失证据的类型、被评估系统的响应以及答案匹配协议的敏感性。为解决这一缺口，我们提出MissDiag，这是一个针对KGQA与KG-RAG中不完全知识鲁棒性的诊断评估框架。MissDiag在保持问题和标准答案固定的同时，对基准提供的支持图应用结构化类型化的缺失干预，从而实现配对比较，将鲁棒性变化分解为证据类型、系统响应和评估协议，而非简化为单一的总体分数下降。对多个系统系列的实验表明，不完全知识鲁棒性应被更好地理解为一种类型化劣化现象，而非均匀属性：答案相邻证据的缺失会产生观测到的最大劣化，源上下文移除通常是中性的，甚至可能有益，语义答案匹配会改变绝对分数，但保留主要的类型化劣化模式。通过将总体鲁棒性测量转化为类型化归因，MissDiag为在不完全知识下比较、诊断和压力测试KGQA与KG-RAG系统提供了更具可解释性的基础。
英文摘要
Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, and incomplete. Existing robustness evaluations usually report aggregate changes in answer quality after evidence is removed or perturbed, which measures sensitivity to incomplete support but leaves the source of degradation under-specified: the same score change can conflate the type of missing evidence, the response of the evaluated system, and the sensitivity of the answer-matching protocol. To address this gap, we propose \textbf{MissDiag}, a diagnostic evaluation framework for incomplete-knowledge robustness in KGQA and KG-RAG. MissDiag keeps the question and gold answer fixed while applying structurally typed missingness interventions to benchmark-provided support graphs, enabling paired comparisons that decompose robustness changes by evidence type, system response, and evaluation protocol rather than reducing them to a single aggregate score drop. Experiments across multiple system families show that incomplete-knowledge robustness is better understood as a typed degradation phenomenon than as a uniform property: answer-adjacent evidence loss produces the largest observed degradation, source-context removal is often neutral and can be beneficial, and semantic answer matching changes absolute scores while preserving the main typed degradation patterns. By transforming aggregate robustness measurement into typed diagnostic attribution, MissDiag provides a more interpretable basis for comparing, diagnosing, and stress-testing KGQA and KG-RAG systems under incomplete knowledge.
