---
title: "A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions"
authors:
  - "Zihan Yang"
  - "Xiaohui Tao"
  - "Taotao Cai"
  - "Yifu Tang"
  - "Haoran Xie"
  - "Lin Li"
  - "Jianxin Li"
  - "Qing Li"
doi: "10.24963/ijcai.2025/1197"
url: "https://ijcai.org/proceedings/2025/1197"
pdf: "https://ijcai.org/proceedings/2025/1197.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10788-10796"
track: "Survey Track"
keywords:
  - "Data Mining: DM: Mining heterogenous data"
  - "Data Mining: General"
  - "Data Mining: DM: Mining graphs"
  - "Data Mining: DM: Knowledge graphs and knowledge base completion"
---

# 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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10788-10796. DOI: [10.24963/ijcai.2025/1197](https://doi.org/10.24963/ijcai.2025/1197)

## 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.