## 4 Experiments and Analysis
### Comparison Models  
This paper compares the proposed model with the following models. The following Tables 3 and 4 illustrate whether the relevant models include modules such as large language models and temporal aggregation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Train** & **Dev** & **Test** \\ \hline Entity Answer & 225,672 & 19,362 & 19,524 \\ Time Answer & 124,328 & 10,638 & 10,476 \\ Simple Entity & 90,651 & 7745 & 7812 \\ Simple Time & 61,471 & 5197 & 5046 \\ Before/After & 23,869 & 1982 & 2151 \\ First/Last & 118,556 & 11,198 & 11,159 \\ Time Join & 55,453 & 3878 & 3832 \\ Total & 350,000 & 30,000 & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CRONQUESTIONS dataset.  
(1) ALBERT [29]: An improved version of BERT that reduces model parameters through parameter sharing and factorized embedding matrices, significantly improving training efficiency.  
(2) EmbedKGQA [30]: Effective in performing multi-hop KGQA on sparse knowledge graphs, it relaxes the requirement to select answers from a pre-specified local neighborhood and uses KG entity embeddings to learn question embeddings. During evaluation, all entities are re-scored, and the entity with the highest score is selected as the answer.  
(3) T5 [31]: T5 (Text-To-Text Transfer Transformer) is a general text generation model proposed by Google in 2019, with the core idea of unifying all NLP tasks into a \"text-to-text\" transformation task. In comparative experiments, the T5-Base model is used, which is a medium-sized version within the T5 series, offering a good balance between performance and efficiency.  
(4) GPT-2 [32]: GPT-2 (Generative Pre-trained Transformer 2) is a natural language processing model based on the Transformer architecture, primarily used for text generation tasks. The comparative experiments utilize the GPT-2 Medium version, which is a medium-sized model within the GPT-2 series developed by OpenAI. Compared to GPT-2 Small, it has more parameters and stronger generative capabilities.  
(5) CronKGQA [5]: Applies a time-sensitive KG embedding algorithm (TCOMPLEX) to obtain embeddings for TKG entities, relations, and timestamps, and uses pre-trained BERT for question processing. CronKGQA uses two scoring functions: one for predicting entities and another for predicting time.  
(6) CTRN [19]: Proposes an improved temporal reasoning method, the Complex Temporal Reasoning Network (CTRN). For each question, it captures implicit temporal features and relational representations and then integrates them to generate implicit temporal relationship representations.  
(7) MultiQA [7]: Introduces the concept of multi-granularity temporal question answering based on knowledge graphs and presents a large-scale dataset for multi-granularity temporal QA. It utilizes TKG embeddings to obtain semantic information and generates different granularity temporal semantic embeddings, using scoring functions to compute candidate answers.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **CronKGQA** & **CTRN** & **MultiQA** & **MTQADC** \\ \hline Large Language Model & yes & yes & yes & yes \\ Embedding Operations & yes & yes & yes & yes \\ Temporal Aggregation & yes & yes & yes & yes \\ Convolutional Network & no & no & no & yes \\ Data Augmentation & no & no & no & yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of different methods.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **ALBERT** & **EmbedKGQA** & **T5** & **GPT-2** \\ \hline Large Language Model & yes & yes & yes & yes \\ Embedding Operations & no & yes & yes & yes \\ Temporal Aggregation & no & no & yes & yes \\ Convolutional Network & no & no & no & no \\ Data Augmentation & no & no & no & yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of different methods.