GNN-based KGQA Query-Side Attacks

Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation

精选理由

这篇论文揭示了GNN-based KGQA在对抗性攻击下的弱点,特别是子图构建阶段,对于理解KGQA系统的鲁棒性具有重要意义。

AI 摘要

研究揭示GNN-based KGQA在实体链接到答案生成各阶段中对抗性攻击下的失败原因,发现子图构建阶段是主要问题所在,而非推理模型。实验在ComplexWebQuestions和WebQSP上展开,结果表明子图构建阶段对攻击更为敏感。相关数据集和评估基础设施已发布。

原文 · arXiv cs.AI

Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation

GNN-based Knowledge Graph Question Answering (KGQA) pipelines process queries through four discrete stages: entity linking, subgraph retrieval, GNN reasoning, and answer generation. Standard robustness evaluations conflate stage-level failures into a single end-to-end metric, obscuring both the source of brittleness and the appropriate mitigation target. We ask which stage fails, and why, when the pipeline is subjected to adversarial perturbations on the input question. We introduce a stage-isolation protocol with two answer-preserving adversarial perturbations verified against the knowledge graph: Compositional Restructuring (CR) and Relation Synonym Swap (RS) target distinct stages while leaving entity seeds intact. Evaluated across ComplexWebQuestions and WebQSP, the results run counter to prevailing assumptions: the GNN reasoning stage retains near-baseline accuracy when the subgraph is intact, while subgraph construction accounts for over 99\% of the end-to-end collapse under CR, occurring even when the gold answer is present in 74\% of retrieved subgraphs. This exposes a fundamental distinction between answer presence and answer reachability that end-to-end metrics cannot detect, and places the mitigation target firmly at the subgraph construction stage rather than the reasoning model. Perturbed datasets and evaluation infrastructure are released at https://anonymous.4open.science/r/atkgrag-E85C .