{"type":"ScholarlyArticle","title":"Learning Multi-Granularity and Adaptive Representation for Knowledge Graph Reasoning","authors":[{"name":"Ziyu Shang","slug":"ziyu-shang","given":"Ziyu","family":"Shang","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Peng Wang","slug":"peng-wang","given":"Peng","family":"Wang","affiliation":"School of Computer Science and Engineering , Southeast University; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education"},{"name":"Wenjun Ke","slug":"wenjun-ke","given":"Wenjun","family":"Ke","affiliation":"School of Computer Science and Engineering , Southeast University; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education"},{"name":"Jiajun Liu","slug":"jiajun-liu","given":"Jiajun","family":"Liu","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Hailang Huang","slug":"hailang-huang","given":"Hailang","family":"Huang","affiliation":"SKLSDE , School of Computer Science and Engineering , Beihang University"},{"name":"Guozheng Li","slug":"guozheng-li","given":"Guozheng","family":"Li","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Chenxiao Wu","slug":"chenxiao-wu","given":"Chenxiao","family":"Wu","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Jianghan Liu","slug":"jianghan-liu","given":"Jianghan","family":"Liu","affiliation":"College of Software Engineering , Southeast University"},{"name":"Xiye Chen","slug":"xiye-chen","given":"Xiye","family":"Chen","affiliation":"College of Information Engineering , Nanjing University of Finance & Economics"},{"name":"Yining Li","slug":"yining-li","given":"Yining","family":"Li","affiliation":"College of Software Engineering , Southeast University"}],"abstract":"Knowledge graph reasoning (KGR) aims to infer new factual triples from existing knowledge graphs (KGs). Recently, a new category of methods, possessing both transductive and inductive reasoning capabilities, has been proposed to tackle this task via learning entity-independent representations from local neighboring structures. However, these methods are plagued by inefficiency issues and they exclusively capture evidence from well-designed local structures, ignoring the correlation between the query and different structures within KGs. In this work, we first propose a novel multi-granularity and adaptive representation framework, MulGA, exploiting the connectivity subgraph to uniformly and hierarchically model query-related triples, relation paths, and subgraphs without explicitly extracting any graph structure, hence mitigating inefficiency issues. Second, we introduce a message-passing mechanism across connectivity subgraphs, facilitating all entities to attain query-related structural representations of diverse granularity levels, i.e., triple and relation paths of different lengths. Third, we design a self-attention-based merging mechanism that allocates weights to different granularities and then consolidates them into subgraph granularity representations for reasoning. The systematic experiments have been conducted on 15 benchmarks and MulGA achieves a significant improvement in MRR by an average of 1.5% on transductive and 2.7% on inductive tasks than existing state-of-the-art methods. Moreover, MulGA boasts faster convergence speed, competitive inference time, and alleviates the over-smoothing prevalent in graph neural networks.","doi":"10.24963/ijcai.2024/258","url":"https://ijcai.org/proceedings/2024/258","pdf":"https://ijcai.org/proceedings/2024/0258.pdf","markdown":"https://ijcai.org/proceedings/2024/258.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/258","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":258,"pages":{"first":2333,"last":2341},"keywords":["Data Mining: DM: Knowledge graphs and knowledge base completion"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Kobi- larov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 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