电力信息与通信技术2026，Vol.24Issue(4)：69-75,7.DOI:10.16543/j.2095-641x.electric.power.ict.2026.04.09
基于主题模型的绿色降碳技术知识图谱构建方法研究
Research on The Construction Method of Green Carbon Reduction Technology Knowledge Graph Based on Topic Model
摘要
Abstract
Addressing the research bottlenecks in knowledge graph construction,such as the difficulty in integrating multi-source heterogeneous data and insufficient semantic mining,this paper proposes a knowledge graph construction method based on the vector-based topic model(VTM).Firstly,a semantic similarity optimization algorithm that incorporates context embeddings from the bidirectional encoder representations from Transformers(BERT)model at the sentence level is designed,achieving semantic disambiguation in terminology normalization.Secondly,a joint extraction mechanism for topic-entity relationships is constructed,enhancing the domain adaptability of entity recognition through dynamic topic constraints.Finally,a conflict resolution algorithm for multi-source entity alignment is designed,and a confidence evaluation model based on semantic vectors is established.When applying the proposed method to the construction of a knowledge graph for green carbon reduction technologies,experimental results show that,compared to existing BERT-based methods,the harmonic mean of precision and recall in named entity recognition and relation extraction tasks is improved by an average of 12.71%and 12.08%,respectively.关键词
主题模型/知识图谱构建/命名实体识别/关系提取Key words
topic model/knowledge graph construction/named entity recognition/relationship extraction分类
信息技术与安全科学引用本文复制引用
张光,张宇鹏,张晓同,张晗,邓桃,陈甜甜,李文清,张学成..基于主题模型的绿色降碳技术知识图谱构建方法研究[J].电力信息与通信技术,2026,24(4):69-75,7.基金项目
国家电网有限公司总部科技项目"新型电力系统电网侧绿色降碳技术创新与标准融合发展模式研究"(1400-202355636A-3-2-ZN). （1400-202355636A-3-2-ZN）
