UBER: Uncertainty-Aware Bridge-structured Evidence Routing for Multi-Knowledge Graph Joint Reasoning

Multi-knowledge graph (multi-KG) joint reasoning aims to answer queries or complete missing facts by exploiting complementary evidence distributed across multiple knowledge graphs. Existing methods mainly focus on cross-KG alignment, global graph integration, or federated representation learning, but often overlook two key challenges: how to route query-specific evidence across different source KGs, and how to reason robustly when cross-KG bridge links are incomplete or unreliable. To address these challenges, we propose UBER, an Uncertainty-aware Bridge-structured Evidence Routing framework for multi-KG joint reasoning. UBER consists of three tightly coupled modules: a Query-aware Source Router (QSR) that dynamically allocates evidence budgets across KGs, an Uncertainty-aware Bridge Constructor (UBC) that models reliable entity- and relation-level bridges, and a Reliability-Gated Fusion Reasoner (RFR) that performs query-conditioned reasoning over compact fused evidence graphs. Instead of globally merging all KGs, UBER constructs a query-specific evidence graph and controls cross-KG propagation according to bridge reliability and relation compatibility. Extensive experiments on three constructed multi-KG completion benchmarks demonstrate that UBER achieves the strongest overall performance over representative non-federated multi-KG, cross-KG, and federated baselines.