[2312.03022] Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction
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Computer Science > Artificial Intelligence

arXiv:2312.03022 (cs)
[Submitted on 5 Dec 2023 (v1), last revised 20 Nov 2024 (this version, v3)]

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

Authors:Hongbin Ye, Honghao Gui, Aijia Zhang, Tong Liu, Weiqiang Jia
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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.
Comments: Accepted by CCKS 2024, best english candidate paper
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2312.03022 [cs.AI]
  (or arXiv:2312.03022v3 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2312.03022

Submission history

From: Hongbin Ye [view email]
[v1] Tue, 5 Dec 2023 07:27:08 UTC (351 KB)
[v2] Fri, 29 Dec 2023 07:34:30 UTC (351 KB)
[v3] Wed, 20 Nov 2024 07:07:41 UTC (294 KB)
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