Knowledge graph construction (KGC) is a multifaceted undertaking involving the extraction of entities, relations, and events. Traditionally, large language models (LLMs) have been viewed as solitary task-solving agents in this complex landscape. However, this paper challenges this paradigm by introducing a novel framework, CooperKGC. Departing from the conventional approach, CooperKGC establishes a collaborative processing network, assembling a KGC collaboration team capable of concurrently addressing entity, relation, and event extraction tasks. Our experiments unequivocally demonstrate that fostering collaboration and information interaction among diverse agents within CooperKGC yields superior results compared to individual cognitive processes operating in isolation. Importantly, our findings reveal that the collaboration facilitated by CooperKGC enhances knowledge selection, correction, and aggregation capabilities across multiple rounds of interactions.

翻译：知识图谱构建（KGC）是一项涉及实体、关系和事件提取的多层面任务。传统上，大型语言模型（LLMs）在这一复杂领域中被视为孤立的任务求解智能体。然而，本文通过引入新颖框架CooperKGC挑战了这一范式。与常规方法不同，CooperKGC构建了一个协同处理网络，组建了一个能够同时处理实体、关系和事件提取任务的KGC协作团队。我们的实验明确表明，在CooperKGC中促进不同智能体间的协作与信息交互，相较于孤立的个体认知过程能产生更优结果。重要的是，我们的研究发现表明，CooperKGC所促进的协作在多轮交互中增强了知识选择、修正和聚合能力。