基于图注意力神经网络的实体消歧方法  

Entity disambiguation method based on graph attention networks

作　　者：牛泽群 李晓戈 强成宇[1] 韩伟 姚怡 刘洋 Zequn NIU;Xiaoge LI;Chengyu QIANG;Wei HAN;Yi YAO;Yang LIU(School of Computer Science and Technology,Xi'an University of Posts and Telecommunications,Xi'an 710121,Shaanxi,China;Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,Xi'an University of Posts and Telecommunications,Xi'an 710121,Shaanxi,China;Xi'an Knowledge Discovery and Application Engineering Technology Center,Xi'an University of Posts and Telecommunications,Xi'an 710121,Shaanxi,China)

机构地区：[1]西安邮电大学计算机学院,陕西西安710121 [2]西安邮电大学陕西省网络数据分析与智能处理重点实验室,陕西西安710121 [3]西安邮电大学西安市知识发现与应用工程技术中心,陕西西安710121

出　　处：《山东大学学报（理学版）》2024年第3期71-80,94,共11页Journal of Shandong University(Natural Science)

基　　金：国家重点研发计划资助项目(2018YFB1402905);陕西省重点研发计划资助项目(2020GY-227);陕西省重点研发计划资助项目(2020ZDLGY09-05);陕西省技术创新引导专项基金(2022PT-49)。

摘　　要：针对链接对象为存在半结构化数据的知识库,提出了一种基于图注意力神经网络的短文本实体指称消歧方法。通过信息抽取与融入关键词,将含有半结构化数据的知识库构建为全局知识图谱;同时基于Bert预训练模型对短文本中的实体指称项进行嵌入融合;使用图注意力神经网络对全局知识图谱中候选实体节点进行加权聚合表征,并计算实体指称项与各候选实体之间的相似度得分,实现实体消歧。在CCKS2019数据集上的实验结果表明,基于图注意力神网络的实体消歧模型有效提高了实体消歧效果。We propose an entity disambiguation method based on graph attention networks for semi-structured knowledge base data.First,a global knowledge graph is constructed from the semi-structured knowledge base,and the entity reference items are embedded by Bert pre-trained model meanwhile.Next,graph attention networks which leverages masked self-attention layers is applyed on candidate entity nodes of global knowledge graph to fetch a vector of node level.Furtherly,we com pute similarity scores rank between the entity reference items and the candidate entity to complete the task of entity disambiguation.The experimental results on CCKS2019 dataset achieve state-of-the-art.

关 键 词：实体消歧 知识图谱 关键词提取 图注意力神经网络 自然语言处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]