TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

Abstract

The TransE model plays a key role in dealing with data sparsity and promotes the development of knowledge graphs completion. However, TransE has some difficulties in dealing with one-to-many, many-to-one, many-to-many and transmission relationships. In order to solve this problem, this paper proposes a knowledge representation learning model based on hyperplane projection and relational attributes, namely TransH-RA. First of all, we introduce the idea of hyperplane projection based on the TransE model, this idea is inspired by TransH, which makes different entities have different roles in a specific relationship, thus reducing the constraints of TransE translation rules, and map the head entity h and tail entity t to the plane of special relation r; Secondly, considering that it is not easy to identify different similar entities, the neighborhood information of entities is added to learn the neighborhood of entities around different entities; Then, in order to further strengthen the ability to deal with complex relationships, attribute features of relationships are added and attribute knowledge is embedded; Eventually, during the training of the model, the probability method is chosen to replace the head and tail entities. Link prediction experiments are conducted on the public datasets FB15K and WN18, and the triple classification experiments on the datasets WN11, FB13 and FB15K are carried out to analyze and verify the effectiveness of the proposed method. The evaluation results show that our method achieves state-of-the-art performance on MeanRank, Hits@10 and ACC indicators compared with TransE and TransH.

Keywords

• Knowledge graphs
• knowledge representation
• hyperplane projection
• relational attributes
• link prediction
• triple classification