摘要
知识图谱推理技术旨在根据已有的知识推导出新的知识,是使机器智能具有和人类一样的推理和决策能力的关键技术之一。系统地研究了知识图谱推理的现代方法,以统一的框架介绍了向量空间中进行知识图谱推理的模型,包括基于几何运算嵌入欧几里得空间和双曲空间的方法,基于卷积神经网络、胶囊网络、图神经网络等深度网络模型的方法。同时,系统地梳理了知识推理技术在各技术领域和各行业的应用情况,指出了当前存在的挑战以及其中蕴含的机会。
Knowledge reasoning over knowledge graph aims to discover new knowledge according to the existing knowledge.It is a pivotal technology to realize the human reasoning and decision-making ability of machine.The modern methods of knowledge reasoning over knowledge graph were studied systematically.And the methods based on vector representations with a unified framework were introduced,including the methods based on embedding into Euclidean space and hyperbolic space,and based on deep learning methods such as convolution neural network,capsule network,graph neural network,etc.Simultaneously,the applications of knowledge reasoning in various technical fields and industries were presented,and the existing challenges and opportunities were pointed out as well.
作者
王文广
WANG Wenguang(DataGrand Inc.,Shanghai 201203,China)
出处
《大数据》
2021年第3期42-59,共18页
Big Data Research
关键词
知识推理
双曲空间嵌入
几何运算
胶囊网络
图神经网络
knowledge reasoning
hyperbolic space embedding
geometric operation
capsule network
graph neural network
