原文:LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities 作者:♣∗ ♣∗ ♣∗ ♣ ♣ ♣ Yuqi Zhu , Xiaohan Wang , Jing Chen , Shuofei Qiao , Yixin Ou , Yunzhi Yao ♡ ♣♠ ♣† Shumin Deng , Huajun Chen , Ningyu Zhang ...
Specifically, while GPT-4 exhibits good performance in tasks related to KG construction, it excels further in reasoning tasks, surpassing fine-tuned models in certain cases. Moreover, our investigation extends to the potential generalization ability of LLMs for information extraction, leading to the...
《Can LLMs be Good Graph Judger for Knowledge Graph Construction?》(https://arxiv.org/pdf/2411.17388)，提出GraphJudger框架来提高KG构建的质量，注意：这个工作与之前schema-based的抽取不同，做的事schema-free的抽取，属于openIE的范畴。如下图所示，其做的更像是一种SPO(subjec,predicate, object)的三...
一、利用大模型判别能力做开放知识图谱构建 《Can LLMs be Good Graph Judger for Knowledge Graph Construction?》(https://arxiv.org/pdf/2411.17388),提出GraphJudger框架来提高KG构建的质量,注意:这个工作与之前schema-based的抽取不同,做的事schema-free的抽取,属于openIE的范畴。 如下图所示,其做的更像是一...
《Can LLMs be Good Graph Judger for Knowledge Graph Construction?》(https://arxiv.org/pdf/2411.17388),提出GraphJudger框架来提高KG构建的质量,注意:这个工作与之前schema-based的抽取不同,做的事schema-free的抽取,属于openIE的范畴。 如下图所示,其做的更像是一种SPO(subjec,predicate, object)的三元组...
《Can LLMs be Good Graph Judger for Knowledge Graph Construction?》(https://arxiv.org/pdf/2411.17388),提出GraphJudger框架来提高KG构建的质量,注意:这个工作与之前schema-based的抽取不同,做的事schema-free的抽取,属于openIE的范畴。 如下图所示,其做的更像是一种SPO(subjec,predicate, object)的三元组...
paper:Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction,https://arxiv.org/pdf/2404.03868v2 code:https://github.com/clear-nus/edc 本文转载自公众号大模型自然语言处理作者:余俊晖 原文链接:https://mp.weixin.qq.com/s/RITUjcKiEy66SL3PK8mFGg...
This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We employ eight distinct datasets that encompass aspects including entity, relation and event extraction, link prediction, and question answering....
TechGPT-2.0: A large language model project to solve the task of knowledge graph construction. 「方法:」研究重点是评估具有减少参数的大型模型在知识图谱构建任务中的性能,旨在为中国开源社区提供一个能够构建知识图谱并保留Chat模型整体能力的实用模型。所有模型都是从LLAMA2派生而来,首先在中文上进行预训练,然后...
也是论文《ITERATIVE ZERO-SHOT LLM PROMPTING FOR KNOWLEDGE GRAPH CONSTRUCTION》的做法。先根据输入文本生成头实体和头实体的相关描述,再从头实体的描述中找到所有和头实体有关的尾实体,最终根据确定的头实体和尾实体确定头实体和尾实体的关系。关键在于根据描述文本确定合适的谓词。谓词最好不要过于具体,也不要过于抽象...