A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions
Zihan Yang, Xiaohui Tao, Taotao Cai, Yifu Tang, Haoran Xie, Lin Li, Jianxin Li, Qing Li
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.
BibTeX
@inproceedings{ijcai2025_asurveyonmultivi,
title = {A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions},
author = {Zihan Yang and Xiaohui Tao and Taotao Cai and Yifu Tang and Haoran Xie and Lin Li and Jianxin Li and Qing Li},
booktitle = {IJCAI 2025},
year = {2025}
}