Abstract: With the development of knowledge graphs,a variety of industrial knowledge graphs have come into being.However,these industrial knowledge graphs lack sufficient relationships among enterprises,such as up-down stream relationship,supply relationship,cooperation and competition relationship,which greatly affects their applications.Most existing methods for predicting the enterprise entity relationships focus on the fact triples and cannot fully utilize multiple perspectives such as enterprise descriptions and associated entity descriptions.To solve this problem,KERP,a knowledge enhanced relationship prediction model for enterprise entities is proposed.The model first improves enterprise features representations using a multi-view entity feature lear-ning module,then uses graph attention network to obtain higher-order semantic representations of entities and fuses lower-order semantic representations learned by TransR for knowledge enhancement,and finally predicts enterprise entity relationships by a convolutional decoder ConvE.Experimental results on the new energy automobile industrial knowledge graph show that KERP has better results in predicting the relationships between enterprises with a improvement of 6.7%in terms of F1 value compared with the existing models.Generalization is also evaluated on multiple datasets,and the experimental results demonstrate that KERP has good generality for generalized entity relationship prediction tasks.

Key words: Industrial knowledge graph, Enterprise entity relationship, Knowledge completion, Link prediction, Knowledge enhancement