计算机技术与发展2026，Vol.36Issue(5)：81-89,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0334
基于关系层级嵌入与多智能体协同的复杂关系抽取
Complex Relation Extraction Based on Relational Hierarchy Embedding and Multi-Agent Collaboration
摘要
Abstract
Sentence-level relation extraction aims to predict semantic relationships between different entities within sentences.Current research faces two major limitations.Existing methods generally overlook hierarchical structural features of relations,restricting models'capability to represent complex semantic relationships,while in relation-intensive scenarios,the escalating complexity of prompt engineering for large language models(LLMs)leads to significant degradation in reasoning efficiency.These deficiencies critically impair the accuracy of complex medical relation extraction.To address these challenges,we propose a hierarchical relation classification approach and develop a collaborative framework integrating both large and small models.Additionally,we design a few-shot learning paradigm that not only constrains LLMs'output formats but also provides demonstration samples.To mitigate error propagation in joint modeling,we introduce a multi-agent collaborative approach to enhance model reliability.Experimental results demonstrate that the proposed relational level classification(RLC)method significantly outperforms existing baseline models.On the CHIP dataset,the ChatGLM model with RLC achieves25.11 percentage points improvement over the original model.Compared to the best-performing relation semantic enhancement method,the RLC method shows superior performance with5.61 and 4.78 percentage points increases in F1-score on the CHIP and BioRel datasets,respectively.Further research indicates that the multi-agent collaborative approach exhibits enhanced representational capabilities,delivering additional improvements of 1.68 and2.78 percentage points in F1-score on the CHIP and BioRel datasets,respectively,when combined with the fine-tuned RLC method.关键词
层级分类/关系抽取/关系语义增强/大模型关系抽取/多智能体协同Key words
hierarchical classification/relation extraction/enhanced relational semantics/large model relation extraction/multi-agent col-laboration分类
信息技术与安全科学引用本文复制引用
高峰,龚天隆,顾进广,张娜,靳英辉..基于关系层级嵌入与多智能体协同的复杂关系抽取[J].计算机技术与发展,2026,36(5):81-89,9.基金项目
国家自然科学基金(U1836118) （U1836118）
湖北省教育厅科学技术研究计划重点项目(D20231104) （D20231104）
武汉市科创局对口科技支援项目(2024071104010831) （2024071104010831）
