Research on Knowledge Map Completion with Entity Description Information based on Neural Network

Knowledge map is a large-scale semantic network that represents knowledge in the form of triples (entity, relationship, entity). It is widely used in information search, question answering system, e-commerce, recommendation system and other fields. In order to alleviate the problem of data sparsity in knowledge map with large amount of data, knowledge map representation learning represents entities and relationships as low dimensional real valued vectors, which effectively reduces the computational complexity and improves the efficiency of knowledge reasoning and knowledge map completion. In order to solve the problem that DKRL does not consider the relationship between sentences, which leads to the serious loss of entity description information, this paper proposes a new model T-CGRU. The model takes advantage of recurrent neural network, which can capture the context information of entity description. Convolution and maximum pooling are used to obtain the feature of entity description, and then the context word order of entity description information is extracted by cyclic operation, and finally the semantic representation of entity description information is obtained.