{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:36Z","timestamp":1758672936212,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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 &amp; 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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1197","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10788-10796","source":"Crossref","is-referenced-by-count":0,"title":["A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions"],"prefix":"10.24963","author":[{"given":"Zihan","family":"Yang","sequence":"first","affiliation":[{"name":"University of Southern Queensland,"},{"name":"University of Melbourne"}]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[{"name":"University of Southern Queensland"}]},{"given":"Taotao","family":"Cai","sequence":"additional","affiliation":[{"name":"University of Southern Queensland"}]},{"given":"Yifu","family":"Tang","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology"}]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[{"name":"Lingnan University"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Edith Cowan University"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:25Z","timestamp":1758627385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1197"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1197","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}