## 5 Conclusions and Future Work  
By introducing temporal information, temporal knowledge graphs can accurately represent the occurrence time, duration, and interrelationships of events, thereby providing users with more accurate and relevant answers based on time series. This paper improves question-answering performance through data augmentation and convolutional feature \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Hits@1**} & \multicolumn{4}{c}{**Hits@10**} \\ \cline{2-11}  & \multirow{2}{*}{**Overall**} & \multicolumn{2}{c}{**Question Type**} & \multicolumn{2}{c}{**Answer Type**} & \multirow{2}{*}{**Overall**} & \multicolumn{2}{c}{**Question Type**} & \multicolumn{2}{c}{**Answer Type**} \\ \cline{3-11}  & & **Complex** & **Simple** & **Entity** & **Time** & & **Complex** & **Simple** & **Entity** & **Time** \\ \hline EmbedKGQA & 0.288 & 0.286 & 0.290 & 0.411 & 0.057 & 0.672 & 0.632 & 0.725 & 0.850 & 0.341 \\ CronKGQA & 0.647 & 0.392 & 0.987 & 0.699 & 0.549 & 0.884 & 0.802 & 0.992 & 0.898 & 0.857 \\ TempoQR Soft & 0.799 & 0.655 & 0.990 & 0.876 & 0.653 & 0.957 & 0.930 & 0.993 & 0.972 & 0.929 \\ TempoQR Hard & 0.918 & 0.864 & 0.990 & 0.926 & 0.903 & 0.978 & 0.967 & 0.993 & 0.980 & 0.974 \\ MTQADC Soft & 0.771 & 0.607 & 0.989 & 0.847 & 0.628 & 0.958 & 0.931 & 0.990 & 0.974 & 0.926 \\ MTQADC Hard & 0.875 & 0.789 & 0.990 & 0.903 & 0.822 & 0.977 & 0.964 & 0.991 & 0.980 & 0.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative experimental results on the CronQuestions dataset. learning. First, by generating different expressions for the same question, the robustness and generalization ability of the question-answering system are enhanced. Paraphrasing helps the system understand temporal semantic variations, enabling it to better handle diverse queries. Additionally, in the task of temporal knowledge graph question answering, convolution operations are used to mine deeper temporal features from raw data, capturing the changing relationships between entities at different time points. Convolution processing of questions gradually enhances the system's understanding of complex temporal relationships, thereby improving the accuracy of the question-answering system. Experiments on public datasets show that the proposed model significantly outperforms existing models, and further experimental analysis demonstrates the functionality and effectiveness of each module in the model. Future work will further optimize the temporal knowledge graph question-answering system: Firstly, the representation capability of temporal information will be enhanced to better capture complex time dependencies. Secondly, advanced model architectures will be utilized to improve the system's reasoning ability and accuracy. Furthermore, the system's application scope will be expanded to cover more dynamic scenarios, such as financial markets, healthcare, etc. Finally, the system's ability to handle large-scale temporal data will be strengthened to improve its efficiency and response speed in real-world applications. Conceptualization, Y.L. and L.S. ; methodology, Y.L. ; software, L.W. ; validation, D.J., L.S. and L.W. ; formal analysis, L.S. ; investigation, Y.L. ; resources, Y.L. ; data curation, L.S. ; writing--original draft preparation, Y.L. ; writing--review and editing, L.S. ; visualization, L.W. ; supervision, D.J. All authors have read and agreed to the published version of the manuscript. This research was funded by China National Natural Science Foundation Project: Research on Question Answering System Based on Deep Transfer Learning, the National Natural Science Foundation of China [Grant numbers 62166021]. Not applicable. The original contributions presented in this study are included in the article, and further inquiries can be directed to the corresponding author. We would like to thank the anonymous reviewers for their valuable and helpful comments, which substantially improved this paper.