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

论文概要

研究领域: AI
作者: Md Saikat Islam Khan Bappy, Oshani Seneviratne
发布时间: 2026-08-25
arXiv: 2608.24846

中文摘要

知识图谱问答的现实世界数据由于治理和数据主权约束通常分布在不同组织中。虽然存在集中式系统，但当所需事实分散在垂直分区的孤岛中时，它们无法回答多跳问题。在本文中，我们提出FedV-KGQA，一种在组织共享实体但拥有不相交关系集的知识图谱上进行多跳推理的框架。我们的方法结合局部图丰富和知识图谱嵌入，确保原始三元组和关系参数永远不会离开每个孤岛，建立结构数据边界而无需集中式图访问。我们进一步引入主题实体锚定机制，将问题锚定在正确的图邻域中而无需任何运行时跨孤岛通信。我们在三个基准上评估12种模型配置，表明FedV-KGQA表现强劲，接近集中式性能，泛化到3跳推理，并对嵌入扰动具有鲁棒性。

原文摘要

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 ...

自动采集于 2026-08-27

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