解决跨组织知识图谱多跳问答难题，无需共享数据即可准确推理。
FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs
- 各机构共享实体但分属不同关系，通过本地增强与嵌入技术实现隐私保护推理。
- 在3个基准上表现接近中心化模型，支持3跳推理且对嵌入扰动稳健。
- 适合医疗、金融等需数据隔离的跨机构知识问答场景。
现实世界中的知识图谱问答数据常因治理和数据主权限制而分布在不同机构中。现有集中式系统无法在关系被垂直分割的孤岛间回答多跳问题。本文提出FedV-KGQA框架，适用于机构共享实体但拥有互不重叠关系集的知识图谱。该方法结合本地图增强与知识图谱嵌入，确保原始三元组和关系参数始终留在本地，建立结构化的数据边界，无需集中式图访问。我们进一步引入主题实体锚定机制，在不依赖运行时跨机构通信的情况下，将问题定位到正确的图邻域。在三个基准上评估12种模型配置，结果表明FedV-KGQA性能优异，接近中心化水平，可泛化至3跳推理，并对嵌入扰动具有鲁棒性。
原文摘要 · Abstract (English)
Real-world data for knowledge graph question answering is often distributed across different organizations due to governance and data sovereignty constraints. While centralized systems exist, they cannot answer multi-hop questions when the required facts are split across vertically partitioned silos. In this paper, we propose FedV-KGQA, a framework for multi-hop reasoning over knowledge graphs in which organizations share entities but own disjoint sets of relations. Our approach combines local graph enrichment and knowledge graph embeddings to ensure raw triples and relation parameters never leave each silo, establishing a structural data boundary without requiring centralized graph access. We further introduce a topic entity anchoring mechanism that grounds questions in the correct graph neighborhood without any runtime inter-silo communication. We evaluate 12 model configurations across three benchmarks and show that FedV-KGQA performs strongly, remains close to centralized performance, generalizes to 3-hop reasoning, and is robust to embedding perturbations.
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