Feb. 15, 2024, 5:46 a.m. | Ruilin Luo, Tianle Gu, Haoling Li, Junzhe Li, Zicheng Lin, Jiayi Li, Yujiu Yang

cs.CL updates on arXiv.org arxiv.org

arXiv:2401.06072v2 Announce Type: replace-cross
Abstract: Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural knowledge. This paper aims to provide a comprehensive perspective on harnessing the advantages of Large Language Models (LLMs) for reasoning in temporal knowledge graphs, presenting an easily transferable pipeline. In terms of graph modality, we underscore the LLMs' prowess in discerning the structural information of pivotal nodes within the historical chain. …

abstract advantages arxiv cs.ai cs.cl event forecasting future graph history knowledge knowledge graph language language models large language large language models llms paper perspective prediction temporal type

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