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Modeling Relation Path for Knowledge Graph via Dynamic Projection

The application of representation learning in knowledge graphs has been a hot topic in recent years. Using representation learning methods, the semantic information contained in knowledge graphs can be embedded into low-dimensional dense vector spaces to achieve the purpose of efficiently processing knowledge graphs. A large number of research results have proved the advantage of the representation learning model represented by the translation model in processing knowledge graph related tasks. However, most translation models focus on the direct relation between entities and ignore the multihop relation between entities in the knowledge graph. In this paper, the relation path between entities in the knowledge graph is modeled. Considering the diversity of entities and relations in the knowledge graph, we embed entities and relations into different semantic spaces, and project the embedding results to the same space dynamically, while maintaining the consistency of the relation path between entities. We use benchmark datasets to evaluate the performance of the proposed model on the task of knowledge completion. The experiment shows that the model proposed in this paper is of great significance to solve the problem of knowledge completion in the knowledge graph.