A knowledge graph completion model based on weighted fusion description information and transform of the dimension and the scale

The existing knowledge graph completion model represents entity and description information by uniform fusion. The convolutional kernel has fewer sliding steps on a triplet matrix composed of entities and relations and does not obtain different-scale characteristics for entities and relations. In this study, a knowledge graph completion model based on weighted fusion description information and the transformation of the dimension and scale, EDMSConvKE, is proposed. First, the entity description information is obtained using the SimCSE model of comparative learning and then combined with the entity according to a certain weight to obtain an entity vector with a stronger expression ability. Second, the head entity, relation, and tail entity vectors are combined into a three-column matrix, and a new matrix is generated using a dimensional transformation strategy to increase the number of sliding steps of the convolution kernel and enhance the information interaction ability of the entity and relation in more dimensions. Third, the multi-scale semantic features of the triples were extracted using convolution kernels of different sizes. Finally, the model in this study was evaluated using a link-prediction experiment, and the model was significantly improved in the Hits@10 and mean rank (MR) indices.