Combining Structure Embedding and Text Semantics for Efficient Knowledge Graph Completion

Knowledge graph completion plays a crucial role in downstream applications. However, existing methods tend to only rely on the structure or textual information, resulting in suboptimal model performance. Moreover, recent attempts to leverage pre-trained language models to complete knowledge graphs have proved unsatisfactory. To overcome these limitations, we propose a novel model that combines structural embedding and semantic information of the knowledge graph. Compared with previous works based on pre-trained language models, our model can better use the implicit knowledge of pre-trained language models by using relation templates, entity definitions, and learnable tokens. Furthermore, our model employs a multihead attention mechanism to transform the embedding semantic space of entities and relations obtained from the knowledge graph embedding model, thereby enhancing their expressiveness and unifying the semantic space of both types of information. Finally, we utilize convolutional neural networks to extract features from the matrices created by combining these two types of information for link prediction and triplet classification tasks. Empirical evaluations on two knowledge graph completion datasets demonstrate that our model is effective for both tasks.