FedV-KGQA框架解决了知识图谱问答中数据分散的问题，结合本地图丰富化和知识图谱嵌入，无需集中式图访问，引入主题实体锚定机制，无需跨孤岛通信即可定位正确图邻域，性能强劲，值得一看。
针对知识图谱问答中数据分散的问题，提出FedV-KGQA框架，结合本地图丰富化和知识图谱嵌入，确保原始三元组和关系参数不离开各个孤岛，实现结构化数据边界，无需集中式图访问。引入主题实体锚定机制，无需跨孤岛通信即可定位正确图邻域。在三个基准上评估12种模型配置，FedV-KGQA表现强劲，接近集中式性能，可泛化到3跳推理，对嵌入扰动具有鲁棒性。
FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs
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.