A survey on multi-view knowledge graph: Generation, fusion, applications and future directions
Author Identifier (ORCID)
Jianxin Li: https://orcid.org/0000-0002-9059-330X
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.
Document Type
Conference Proceeding
Date of Publication
1-1-2025
Publication Title
IJCAI International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence
School
School of Business and Law
RAS ID
84305
Copyright
free_to_read
First Page
10788
Last Page
10796
Comments
Yang, Z., Tao, X., Cai, T., Tang, Y., Xie, H., Li, L., Li, J., & Li, Q. (2025). A survey on multi-view knowledge graph: generation, fusion, applications and future directions. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 10788-10796). https://doi.org/10.24963/ijcai.2025/1197