摘要
知识图谱作为认知智能的关键技术,在知识表示与处理领域展现出了巨大的应用潜力,图结构凭借其强大的表达能力与灵活的拓扑特性,为知识图谱的构建提供了有效的计算方法与技术支撑。基于传统知识图谱构建方法在处理大规模异构数据时存在的局限性,引入图神经网络等深度学习技术能有效提升知识获取与知识融合的准确度,通过图注意力机制与关系感知的方式,增强知识图谱在实体识别与实体对齐等任务中的表现。融合图结构特征的知识图谱构建方法不仅提高了知识表示的准确性,还在知识推理与链接预测等下游任务中体现出了优越性,推动了知识图谱技术在实际应用中的深化发展。
As a key technology in cognitive intelligence,knowledge graphs demonstrate significant application potential in the field of knowledge representation and processing.Graph structures,with their powerful expressive capabilities and flexible topological characteristics,provide effective computational methods and technical support for building knowledge graphs.Traditional knowledge graph construction methods have limitations when dealing with large-scale heterogeneous data.Introducing deep learning techniques such as graph neural networks can effectively enhance the accuracy of knowledge acquisition and integration.By employing graph attention mechanisms and relationship perception,these methods improve the performance of knowledge graphs in tasks like entity recognition and entity alignment.Knowledge graph construction methods that integrate graph structure features not only increase the accuracy of knowledge representation but also show superiority in downstream tasks such as knowledge reasoning and link prediction,promoting the deep development of knowledge graph technology in practical applications.
作者
韦萌萌
秦榕霞
苏俊琦
WEI Mengmeng;QIN Rongxia;SU Junqi(Huanghe Jiaotong University,Jiaozuo Henan 454950)
出处
《软件》
2025年第6期22-24,59,共4页
Software
基金
2022年度黄河交通学院校级课题“离散数学一流课程项目”(HHJTXY-2022ylkc55)。
关键词
知识图谱
图神经网络
图结构表示
链接预测
图注意力机制
knowledge graph
graph neural network
graph structure representation
link prediction
graph attention mechanism
