Multi-relational knowledge graph reasoning algorithm based on representation learning

In the current reasoning methods of knowledge graph, representation learning, especially the TransE series of algorithms based on translation, has achieved excellent performance. Most of the related papers focus on entity reasoning, however, relational reasoning as a key technology for knowledge graph completion deserves attention and research. At the same time, in the knowledge graph with ever-expanding scale and more diversified sources of knowledge, there are many more and complex types of relations, and the frequency of one kind of relations in all triples is further reduced, which increases the difficulty of relational reasoning.Therefore, for the multi-relational knowledge graph, based on the TransE model and focusing on the relational reasoning, a new relationship modeling method is proposed, which can alleviate the problem of multiple relations competing for the same vector in the multi-mapping attribute relations. Then combined with other methods to make the new model feasible in entity reasoning. Through the knowledge reasoning experiments carried out on the public FB15k data set and the Chinese data set extracted from the network, comparing the accuracy of relational reasoning and entity reasoning with similar methods, good results have been achieved and it successfully verify the effectiveness and advancement of the proposed algorithm.