吉林大学学报（理学版）2025，Vol.63Issue(2)：465-471,7.DOI:10.13413/j.cnki.jdxblxb.2024062
面向领域知识图谱的实体关系抽取模型仿真
Simulation of Entity Relationship Extraction Model for Domain Knowledge Graph
摘要
Abstract
Aiming at the problem of poor performance of entity relationship extraction in current domain knowledge graphs,we proposed a research method for entity relationship extraction models oriented towards domain knowledge graphs.Firstly,we established an entity relationship extraction model consisting of an encoding and decoding module,an entity recognition module,and an entity relationship extraction module.In the entity relationship extraction model,a bidirectional long short-term memory neural network was used to encode text sentences,and the feature representation vectors of the encoded text sentences were input into a deep neural network-based entity recognition module for entity recognition of text sentences,and the recognition results were input into the entity relationship extraction module based on convolutional neural networks for entity relationship extraction.Secondly,the entity relationship triplet obtained from entity relationship extraction was input into the encoding and decoding module for decoding operation,achieving the final entity relationship extraction for domain oriented knowledge graph.The experimental results show that the proposed method has better entity relationship extraction effect and overall application effect.关键词
知识图谱/实体关系抽取/实体识别/卷积神经网络Key words
knowledge graph/entity relationship extraction/entity recognition/convolutional neural network分类
信息技术与安全科学引用本文复制引用
何山,肖晰,张嘉玲..面向领域知识图谱的实体关系抽取模型仿真[J].吉林大学学报（理学版）,2025,63(2):465-471,7.基金项目
国家自然科学基金面上项目(批准号:62276099). （批准号:62276099）
