## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.3 Ablation Experiment Results and Analysis  
This section presents the ablation experiment results of the MTQADC model, as shown in Table 7 and Figure 7. The effectiveness of the model's data augmentation, convolutional feature learning, and multi-granularity time aggregation on temporal knowledge graph question answering is explored.  
First, removing the data augmentation module shows a noticeable decline in model performance on the Hits@1 and Hits@10 evaluation metrics, with decreases of 0.021 and 0.018, respectively. In terms of multi-granularity time, the performance also drops for the \"Equal\" question type, with reductions of 0.031, 0.007, and 0.039 at the daily, monthly, and yearly granularities, respectively. These results indicate that data augmentation enriches the question representation and improves the question-answering performance in temporal knowledge graph tasks.  
Next, removing the convolutional feature learning module causes the most significant drop in model performance compared to MTQADC. The Hits@1 and Hits@10 metrics decreased by 0.039 and 0.026, respectively. For multi-granularity time, the \"Before/After\" question type shows declines of 0.081, 0.084, and 0.103 at the daily, monthly, and yearly  
\begin{table}
\begin{tabular}{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{2-10}  & **Overall** & **Question Type** & **Answer Type** & **Overall** & **Question Type** & **Answer Type** \\ \cline{2-10}  & **Multiple** & **Single** & **Entity** & **Time** & **Multiple** & **Single** & **Entity** & **Time** \\ \hline MTQADC & 0.342 & 0.185 & 0.406 & 0.430 & 0.128 & 0.633 & 0.532 & 0.674 & 0.742 & 0.369 \\ \hline w/o Data Augmentation & 0.321 & 0.160 & 0.386 & 0.409 & 0.105 & 0.615 & 0.527 & 0.615 & 0.740 & 0.311 \\ \hline w/o Convolutional Networks & 0.303 & 0.164 & 0.359 & 0.381 & 0.113 & 0.607 & 0.503 & 0.650 & 0.721 & 0.331 \\ \hline w/o Temporal Aggregation & 0.320 & 0.175 & 0.378 & 0.417 & 0.083 & 0.607 & 0.518 & 0.644 & 0.735 & 0.298 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation Experiment Results.  
Figure 7: Ablation study of multi-granularity time for different types of questions under the Hits@1 metric: (**a**) ablation study for Equal types of questions. (**b**) Ablation study for Before/After types of questions. (**c**) Ablation study for Equal Multi types of questions. (**d**) Ablation study for overall questions.  
granularities, respectively. This further demonstrates that the CNN can effectively capture latent relational features in time series data. In temporal knowledge graph question-answering tasks, CNNs extract richer temporal features through convolution operations, capturing the dynamic relationships between entities at different time points.  
Finally, removing the multi-granularity time aggregation module leads to decreases of 0.022 and 0.026 in the Hits@1 and Hits@10 metrics, respectively. As shown in the table, the multi-granularity time aggregation module has the most significant impact on time-related question types, with reductions of 0.035 in Hits@1 and 0.071 in Hits@10. This highlights that the time aggregation module improves the learning of time information, thereby enhancing the experiment's performance.