Convolutional Network Embedding of Text-Enhanced Representation for Knowledge Graph Completion

Abstract

In recent years, knowledge graph representation learning has prompted extensive research. Machine learning models are used to map entity and relational data in knowledge graphs to vector representations in low-dimensional spaces to predict and analyze potential relationships. Current works mainly focus on the knowledge representation of the triple structure and relationship path in knowledge graphs without fully utilizing external textual information to semantically supplement knowledge representation. However, the existing knowledge inventory, such as that for smart health and emotion care systems, is relatively meager, and structural knowledge is incomplete; therefore, knowledge graph completion is essential. In this article, we propose a novel joint representation learning model that introduces text description information and extracts reliable feature information from text data by using a convolutional neural network (CNN) model. Furthermore, being based on the attention mechanism, the proposed model distinguishes the characteristic credibility of different relationships, enhances the representation of the entity relationship structure vector in the existing knowledge graph, and obtains rich semantic information. Finally, the 2-D convolution operation is used to process the joint representation vectors of entities and relationships to obtain nonlinear features, and the knowledge graph is completed by completing the calculation of the score function of the joint representation vector of the entity and the relationship. Experiments performing tasks, such as link prediction and triple classification, on the FreeBase (FB15k), WordNet (WN18) and Yet Another Great Ontology (YAGO3-10) data sets reveal that our model performs better than the benchmark model and has some degree of scalability.