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

• URL: http://arxiv.org/abs/2312.03022v3
• Date: Wed, 20 Nov 2024 07:07:41 GMT
• Title: Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction
• Authors: Hongbin Ye, Honghao Gui, Aijia Zhang, Tong Liu, Weiqiang Jia,
• Abstract summary: CooperKGC is a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC) CooperKGC establishes a collaborative processing network, assembling a team capable of concurrently addressing entity, relation, and event extraction tasks.
• Score: 6.020016097668138
• License:
• Abstract: This paper introduces CooperKGC, a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC). CooperKGC establishes a collaborative processing network, assembling a team capable of concurrently addressing entity, relation, and event extraction tasks. Experimentation demonstrates that fostering collaboration within CooperKGC enhances knowledge selection, correction, and aggregation capabilities across multiple rounds of interactions.

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