A Knowledge Graph Embedding Framework With Triple Semantics

Abstract

The knowledge graph embedding model aims to use low-dimensional real-valued vectors to represent the entities and relations in the triples, where operations such as link prediction and triple classification can be performed based on these representations. However, existing embedding models only consider the structural embedding of triples, while ignoring the semantic information of triples. This paper proposes a knowledge graph embedding learning framework combined with triple semantic information (KGSE). KGSE comprehensively considers the structural embedding and semantic embedding of triples, where semantic embedding is used as a supplement to improve the quality of embedding. Specifically, KGSE uses the improved TransD model to obtain the structural embedding of triples, and employs the deep convolutional neural model combined with an attention mechanism to obtain the semantic embedding of triples. In addition, a novel energy function is designed to jointly train the above two embeddings. Experimental results show that the proposed framework improves significantly compared with Trans-based models and other baseline models in link prediction and triple classification tasks, which verifies the effectiveness of the proposed framework.