## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.1 Comparative Experimental Results and Analysis
\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{2-11}  & \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}  & & **Multiple** & **Single** & **Entity** & **Time** & & **Multiple** & **Single** & **Entity** & **Time** \\ \hline ALBERT & 0.108 & 0.086 & 0.116 & 0.139 & 0.032 & 0.484 & 0.415 & 0.512 & 0.589 & 0.228 \\ EmbedKGQA & 0.206 & 0.134 & 0.235 & 0.290 & 0.001 & 0.459 & 0.439 & 0.467 & 0.648 & 0.001 \\ T5 & 0.279 & 0.175 & 0.322 & 0.379 & 0.036 & 0.560 & 0.497 & 0.585 & 0.707 & 0.201 \\ GPT-2 & 0.283 & 0.161 & 0.333 & 0.362 & 0.091 & 0.586 & 0.489 & 0.625 & 0.702 & 0.304 \\ CronKGQA & 0.279 & 0.134 & 0.337 & 0.328 & 0.156 & 0.608 & 0.453 & 0.671 & 0.696 & 0.392 \\ CTRN & 0.307 & 0.177 & 0.359 & 0.387 & 0.110 & 0.611 & 0.507 & 0.653 & 0.723 & 0.338 \\ MultiQA & 0.293 & 0.159 & 0.347 & 0.349 & **0.157** & **0.635** & 0.519 & **0.682** & 0.733 & **0.396** \\ \hline MTQADC & **0.342** & **0.185** & **0.406** & **0.430** & 0.128 & 0.633 & **0.532** & 0.674 & **0.742** & 0.369 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results on the MULTITQ dataset. beddings and is more suitable for traditional knowledge graphs. In contrast, CronKGQA, MultiQA, and MTQADC all use time-sensitive embedding methods like TComplEx, which incorporate time embeddings, allowing for better processing and extraction of time-related information and making them more suitable for embedding temporal knowledge graphs. (3) The T5 model employs a multi-granularity temporal aggregation module. Compared to the MTQADC model, the T5 model is lower by 0.063 and 0.073 in the Hits@1 and Hits@10 metrics, respectively. The T5 model performs well on Multiple-type questions, being only 0.01 lower than the MTQADC model in the Hits@1 metric. However, for Time-type questions, the T5 model's performance is 0.092 lower than that of the MTQADC model in the Hits@1 metric, indicating that the T5 model has a general performance in answering time-related questions. From the perspective of temporal multi-granularity, the T5 model performs well on Before/After questions, achieving Hits@1 metric values of 0.611, 0.637, and 0.637 for day, month, and year granularity questions, respectively, which is second only to the MTQACD model. (4) In terms of overall question-answering performance, the GPT-2 model is lower than the MTQADC model by 0.059 in the Hits@1 metric and by 0.047 in the Hits@10 metric. For Single-type questions, the GPT-2 model is lower than the MTQADC model by 0.073 in the Hits@1 metric. In the Entity question type, the GPT-2 model is lower than the MTQADC model by 0.068 in the Hits@1 metric. From the perspective of temporal multi-granularity, the GPT-2 model has a Hits@1 of 0.33 for the Equal-type month granularity, which is 0.063 lower than that of the MultiQA model. (5) CronKGQA, MultiQA, and MTQADC all use time-sensitive embedding algorithms, but as shown in the table, CronKGQA performs worse compared to MultiQA and MTQADC. CronKGQA is 0.063 and 0.025 lower than MTQADC in Hits@1 and Hits@10, respectively. In terms of time granularity, for the Before/After question type, CronKGQA is lower than MTQADC by 0.265, 0.233, and 0.273 in yearly granularity. This is because CronKGQA was initially designed to handle questions with a single-time granularity, such as temporal knowledge graph questions answered with only yearly granularity. Therefore, it performs poorly on multi-granularity temporal knowledge graph question answering. In contrast, MultiQA and MTQADC use the multi-granularity time aggregation module, which allows for the aggregation of different time granularities, resulting in better performance on multi-granularity questions.