{"type":"ScholarlyArticle","title":"A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions","authors":[{"name":"Zihan Yang","slug":"zihan-yang","given":"Zihan","family":"Yang","affiliation":""},{"name":"Xiaohui Tao","slug":"xiaohui-tao","given":"Xiaohui","family":"Tao","affiliation":""},{"name":"Taotao Cai","slug":"taotao-cai","given":"Taotao","family":"Cai","affiliation":""},{"name":"Yifu Tang","slug":"yifu-tang","given":"Yifu","family":"Tang","affiliation":""},{"name":"Haoran Xie","slug":"haoran-xie","given":"Haoran","family":"Xie","affiliation":""},{"name":"Lin Li","slug":"lin-li","given":"Lin","family":"Li","affiliation":""},{"name":"Jianxin Li","slug":"jianxin-li","given":"Jianxin","family":"Li","affiliation":""},{"name":"Qing Li","slug":"qing-li","given":"Qing","family":"Li","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/1197","url":"https://ijcai.org/proceedings/2025/1197","pdf":"https://ijcai.org/proceedings/2025/1197.pdf","markdown":"https://ijcai.org/proceedings/2025/1197.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1197","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Survey Track","track":"Survey Track","number":1197,"pages":{"first":10788,"last":10796},"keywords":["Data Mining: DM: Mining heterogenous data","Data Mining: General","Data Mining: DM: Mining graphs","Data Mining: DM: Knowledge graphs and knowledge base completion"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}