A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions

Abstract

Knowledge Graphs (KGs) have revolutionized structured knowledge representation, yet their capacity to model real-world complexity and heterogeneity remains fundamentally constrained. The emerging paradigm of Multi-View Knowledge Graphs (MVKGs) addresses this gap through multi-view learning, but existing research lacks systematic integration. This survey provides the first systematic consolidation of MVKG methodologies, with four pivotal contributions: 1) The first unified taxonomy of view generation paradigms that rigorously categorizes view into four types: structure, semantic, representation, and knowledge & modality; 2) A novel methodological typology for view fusion that systematically classifies techniques by fusion targets (feature, decision, and hybrid); 3) Task-centric application mapping that bridges theoretical MVKG constructs to node/link/graph-level downstream tasks; 4) A forward-looking roadmap identifying underexplored challenges. By unifying fragmented methodologies and formalizing MVKG design principles, this survey serves as a roadmap for advancing KG versatility in complex AI-driven scenarios. In doing so, it paves the way for more efficient knowledge integration, enhanced decision-making, and cross-domain learning in real-world applications.