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.

MOTIVATION: Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs. RESULTS: To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision-recall curves. AVAILABILITY AND IMPLEMENTATION: Code and data are available at: https://github.com/galaxysunwen/MSTE-master.