SFP: temporal knowledge graph completion based on sequence-focus patterns representation learning

The extrapolation task in the temporal knowledge graph has received increasing attention from scholars due to its wide range of practical application scenarios. At present, recurrent neural networks are currently widely used in temporal knowledge graph completion techniques. These networks are employed to depict the sequential pattern of entities and relations. However, as the sequence lengthens, some critical early information may become diluted. Prediction errors ensue in the completion task as a result. Furthermore, it is observed that existing temporal knowledge graph completion methods fail to account for the topological structure of relations, which leads to relation representations with essentially little distinction across different timestamps. In order to tackle the previously mentioned concern, our research introduces a Temporal Knowledge Graph Completion Method utilizing Sequence-Focus Patterns Representation Learning (SFP). This method contains two patterns: the Focus pattern and the Sequential pattern. In the SFP model, we developed a novel graph attention network called ConvGAT. This network efficiently distinguishes and extracts complex relation information, thereby enhancing the accuracy of entity representations that are aggregated in the Focus pattern and Sequential pattern. Furthermore we proposed RelGAT, a graph attention network that simulates the topological structure of relations. This enhances the precision of relation representations and facilitates the differentiation between relation embeddings generated at various timestamps in the Focus pattern. Utilizing a time-aware attention mechanism, the Focus pattern extracts vital information at particular timestamps in order to amplify the data that the Sequential pattern dilutes. On five distinct benchmark datasets, SFP significantly outperforms the baseline, according to a comprehensive series of experiments.