关键词: 工业云机器人, 知识图谱, TransE算法, 服务推荐

Abstract: With the widespread application of industrial cloud robots,the explosive growth of service information has caused service information overload.Quick and efficient recommending high-quality and reliable services to users from a large number of manufacturing services is very difficult.A recommendation method for industrial cloud robot manufacturing capability service based on knowledge representation learning was proposed,which started from the construction of industrial cloud robot manufacturing capability service knowledge graph,and analyzed industrial cloud robot manufacturing task information,manufacturing capability information,service function information and non-function information.To improve the speed of knowledge recommendation,the knowledge representation learning was introduced.It represented entities and relationships as low-dimensional dense vectors to enable efficient computation of semantic associations between entities and relationships.Aiming at the problem that the random replacement method used to generate negative samples in most translation models leaded to the low prediction accuracy of the model,a new negative sample generation strategy was proposed,which was combined with TransE to verify the negative sample generation strategy on the FB15K.The experimental results showed that it had higher accuracy in link prediction.

Key words: industrial cloud robotics, knowledge graph, TransE algorithm, service recommendation