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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.03022 |
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| _version_ | 1866915026717114368 |
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| author | Ye, Hongbin Gui, Honghao Zhang, Aijia Liu, Tong Jia, Weiqiang |
| author_facet | Ye, Hongbin Gui, Honghao Zhang, Aijia Liu, Tong Jia, Weiqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03022 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction Ye, Hongbin Gui, Honghao Zhang, Aijia Liu, Tong Jia, Weiqiang Artificial Intelligence Computation and Language Machine Learning 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. |
| title | Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2312.03022 |