Research On Joint Representation Learning Model Of Knowledge Graph Based On Text Information Enhancement

The concept of knowledge graph is derived from the knowledge base,and the data entities are linked together through mutual relationships.Knowledge graph technology aims to store complex and related information between entities in the open world.It can improve the accuracy and efficiency of data query in the existing knowledge base.It has wide application value in the fields of automatic knowledge question answering and recommendation systems.The existing knowledge graph is usually imperfect,and the data association is sparse,resulting in its poor performance in application systems such as automatic question answering and intelligent recommendation.The textenhanced representation learning technology makes full use of the rich semantic information of text data,and merges the text information associated with the knowledge base,which can enhance the semantic representation of the entity relationship vector and complete the sparse structure data of the knowledge graph.It can improve the accuracy of knowledge inference calculation and inference in intelligent systems.In order to utilize the rich text data information outside the knowledge graph to semantically enhance the entity relationship structure vector in the knowledge graph obtained by representation learning,a knowledge joint representation learning model is established.Using the idea of translation training algorithm to learn to get the representation vector of the triple structure inside the knowledge graph.Aiming at the text description information of related concepts of knowledge graph,a convolutional neural network is designed to extract the reliable feature information in the sentence,the output vector is processed with reasonable convolution kernel parameters,and the text representation vector is mapped to the embedding space consistent with the structure vector.Based on the attention mechanism,distinguish the credibility of the features of different relationships,and assign weight parameters according to the degree of relevance of each text to the relationship.The semantic combination is carried out through the calculation of the inner product of the vector,so that the relation related text embedding vector in the knowledge graph can be effectively obtained.The joint model uses the representation vectors of related texts to perform enhanced representation learning on the entity relationship structure vectors in the existing knowledge base,so that the translation vectors of the knowledge representation model are more semantically interpretable.It can also use the external modal information of the knowledge graph to calculate and complete the sparse domain knowledge of the existing knowledge graph.At the same time,the model uses the 2-dimensional convolution operation to process the joint representation vector of entities and relationships,extract the non-linear characteristics of the vector itself,enhance the interaction between implicit vectors,and have efficient parameter utilization efficiency.It alleviates the high complexity of complex relational data modeling to a certain extent.In order to verify the validity of the calculation model,a comparison experiment with the general model Trans E is conducted on the FB15 k,WN18 and YAGO3-10 datasets respectively.The overall prediction accuracy on entity prediction task is improved by 6%-20%,and the accuracy on triple classification task is improved by 4%-12%,which fully clarifies the effectiveness and scalability of the joint representation model.