A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions
Paper
| Paper/Presentation Title | A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions |
|---|---|
| Presentation Type | Paper |
| Authors | Yang, Zihan, Tao, Xiaohui, Cai, Taotao, Tang, Yifu, Xie, Haoran, Li, Lin, Li, Jianxin and Li, Qing |
| Journal or Proceedings Title | Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI-24) |
| Journal Citation | pp. 10788-10796 |
| Article Number | 1197 |
| Number of Pages | 9 |
| Year | 2024 |
| Place of Publication | Korea |
| ISBN | 9781956792041 |
| Digital Object Identifier (DOI) | https://doi.org/10.24963/ijcai.2025/1197 |
| Web Address (URL) of Paper | https://www.ijcai.org/proceedings/2025/1197 |
| Web Address (URL) of Conference Proceedings | https://www.ijcai.org/Proceedings/2024/ |
| Conference/Event | 33rd International Joint Conference on Artificial Intelligence (IJCAI-24) |
| Event Details | 33rd International Joint Conference on Artificial Intelligence (IJCAI-24) Parent International Joint Conference on Artificial Intelligence Delivery In person Event Date 03 to end of 09 Aug 2024 Event Location Jeju, Korea Event Web Address (URL) |
| 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. |
| Keywords | Data Mining: DM: Knowledge graphs and knowledge base completion; Data Mining: DM: Mining graphs; Data Mining: DM: Mining heterogenous data |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field. |
| Byline Affiliations | University of Southern Queensland |
| University of Melbourne | |
| School of Science, Engineering and Digital Technologies | |
| Swinburne University of Technology | |
| Lingnan University of Hong Kong, China | |
| Wuhan University of Technology, China | |
| Edith Cowan University | |
| Hong Kong Polytechnic University, China |
https://research.usq.edu.au/item/1013x0/a-survey-on-multi-view-knowledge-graph-generation-fusion-applications-and-future-directions
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