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FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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 l

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