{"type":"ScholarlyArticle","title":"LLM-based Multi-Level Knowledge Generation for Few-shot Knowledge Graph Completion","authors":[{"name":"Qian Li","slug":"qian-li","given":"Qian","family":"Li","affiliation":"State Key Laboratory of Networking and Switching Technology , Beijing University of Posts and Telecommunications , Beijing , China; School of Computer Science and Engineering , Beihang University , Beijing , China"},{"name":"Zhuo Chen","slug":"zhuo-chen","given":"Zhuo","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University , Zhejiang , China"},{"name":"Cheng Ji","slug":"cheng-ji","given":"Cheng","family":"Ji","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China"},{"name":"Shiqi Jiang","slug":"shiqi-jiang","given":"Shiqi","family":"Jiang","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China"},{"name":"Jianxin Li","slug":"jianxin-li","given":"Jianxin","family":"Li","affiliation":"School of Computer Science and Engineering , Beihang University , Beijing , China; Zhongguancun Lab , Beijing , China"}],"abstract":"Knowledge Graphs (KGs) are pivotal in various NLP applications but often grapple with incompleteness, especially due to the long-tail problem where infrequent, unpopular relationships drastically reduce the KG completion performance. In this paper, we focus on Few-shot Knowledge Graph Completion (FKGC), a task addressing these gaps in long-tail scenarios. Amidst the rapid evolution of Large Language Models, we propose a generation-based FKGC paradigm facilitated by LLM distillation. Our MuKDC framework employs multi-level knowledge distillation for few-shot KG completion, generating supplementary knowledge to mitigate data scarcity in few-shot environments. MuKDC comprises two primary components: Multi-level Knowledge Generation, which enriches the KG at various levels, and Consistency Assessment, to ensure the coherence and reliability of the generated knowledge. Most notably, our method achieves SOTA results in both FKGC and multi-modal FKGC benchmarks, significantly advancing KG completion and enhancing the understanding and application of LLMs in structured knowledge generation and assessment.","doi":"10.24963/ijcai.2024/236","url":"https://ijcai.org/proceedings/2024/236","pdf":"https://ijcai.org/proceedings/2024/0236.pdf","markdown":"https://ijcai.org/proceedings/2024/236.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/236","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":236,"pages":{"first":2135,"last":2143},"keywords":["Data Mining: DM: Knowledge graphs and knowledge base completion","Natural Language Processing: NLP: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Al- berto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 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