Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction

• URL: http://arxiv.org/abs/2312.03022v2
• Date: Fri, 29 Dec 2023 07:34:30 GMT
• Title: Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction
• Authors: Hongbin Ye, Honghao Gui, Aijia Zhang, Tong Liu, Wei Hua, Weiqiang Jia
• Abstract summary: This paper introduces a novel framework, CooperKGC, for knowledge graph construction. 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.
• Score: 10.1305370182537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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