关键词: 蔬菜, 知识图谱, 知识表示学习, TransE, PTA模型, 链接预测

Abstract:  Since the TransE model ignores the semantic differences of vectors of the same class when dealing with the relationship between different categories, it has poor performance in the representation learning of the complex attribute relations of onetomany, manytoone, and manytomany existing in the knowledge graph of the vegetable domain. Based on the TRANSE method, PTA (pathbased TransE for Attribute) model was proposed in this paper to improve it. Firstly, the vegetable domain entities and relationships were mapped into a lowdimensional dense vector space by Word2Vector. Secondly, the attribute relation and the upanddown relation were combined to form the relation path for training. Mean Rank and Hits@10 were used to evaluate the link prediction effect of the presentation learning model. The experimental results showed that compared with the TransE model, the PTA model was better than the TransE model without considering the relation classification, and the Mean Rank was up to 6 orders advanced. In the case that attribute relationships were classified according to complexity, the value of link prediction Hits@10was up to 13% higher than that of the TransE model.

Key words: vegetable, knowledge graph, representation learning, TransE model, PTA model, link prediction