Knowledge Graph Completion Based on Contrastive Learning and Language Model-Enhanced Embedding

Abstract

A knowledge graph is a structured knowledge base comprising various types of knowledge or data units obtained through extraction and other processes. It is used to describe and represent information, such as entities, concepts, facts, and relationships. The limitations of Natural Language Processing(NLP) technology and the presence of noise in the texts of various knowledge or information units affect the accuracy of information extraction. Existing Knowledge Graph Completion(KGC) methods typically account for only single structural information or text semantic information, whereas the structural and text semantic information in the entire knowledge graph is disregarded. Hence, a KGC model based on contrastive learning and language model-enhanced embedding is proposed. The input entities and relationships are obtained using a pretrained language model to obtain the textual semantic information of the entities and relationships. The distance scoring function of the translation model is used to capture the structured information in the knowledge graph. Two negative sampling methods for contrastive learning are used to fuse contrastive learning to train the model to improve its ability to represent positive and negative samples. Experimental results show that compared with the Bidirectional Encoder Representations from Transformers for Knowledge Graph completion(KG-BERT) model, this model improves the average proportion of triple with ranking less than or equal to 10(Hits@10) indicator by 31% and 23% on the WN18RR and FB15K-237 datasets, respectively, thus demonstrating its superiority over other similar models.