# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 4 Performance Comparison of Deep Learning-Based TKGC Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **TKGC Method** & **MRR** & **MR** & **Hits@10** & **Hits@1** \\ \hline \multirow{3}{*}{LSTM-based methods} & TA-TransE ([PERSON] et al., 2018) \({}^{*}\) & 0.299 & 79 & 66.8 & 9.6 \\  & TA-DISTMULT ([PERSON] et al., 2018)\({}^{*}\) & 0.474 & 98 & 72.8 & 34.6 \\  & TA-TransR ([PERSON] et al., 2018) & - & - & - & - \\ \hline \multirow{3}{*}{CNN-based methods} & CATKGC ([PERSON] et al., 2018) & - & - & - & - \\  & ConvTKG ([PERSON] et al., 2018)\({}^{0}\) & 0.740 & - & 86.2 & 67.4 \\  & SANe ([PERSON] et al., 2018)\({}^{*}\) & 0.683 & - & 82.3 & 60.5 \\  & SANe\({}^{+}\)([PERSON] et al., 2018)\({}^{*}\) & 0.686 & - & 82.6 & 60.8 \\ \hline \multirow{3}{*}{CapsNet-based methods} & TempCaps ([PERSON] et al., 2018)\({}^{*}\) & 0.521 & - & 70.5 & 42.3 \\  & BiQCap ([PERSON] et al., 2018)\({}^{*}\) & 0.691 & - & 83.7 & 62.1 \\ \hline \multirow{3}{*}{Attention-based methods} & RoAN ([PERSON] et al., 2018)\({}^{*}\) & 0.599 & - & 82.3 & 47.9 \\  & HSAE ([PERSON] et al., 2018)\({}^{*}\) & 0.864 & - & 94.1 & 81.7 \\ \hline \multirow{3}{*}{GNN-based methods} & TGAP ([PERSON] et al., 2018)\({}^{*}\) & 0.670 & - & 84.5 & 56.8 \\  & STRGNN ([PERSON] et al., 2018)\({}^{*}\) & 0.781 & - & 85.1 & 66.7 \\  & HyGNet ([PERSON] et al., 2018)\({}^{*}\) & 0.693 & - & 83.7 & 61.2 \\ \hline \multirow{3}{*}{GCN-based methods} & TeMP ([PERSON] et al., 2018)\({}^{*}\) & 0.691 & - & 91.7 & 56.6 \\  & MGCN ([PERSON] et al., 2018)\({}^{*}\) & - & - & - & - \\  & TAGCN ([PERSON] et al., 2018)\({}^{*}\) & 0.641 & - & 81.2 & 55.3 \\  & TAL-TKGC ([PERSON] et al., 2018)\({}^{*}\) & 0.354 & - & 69.2 & 22.6 \\  & THOR ([PERSON] et al., 2018)\({}^{*}\) & 0.799 & - & 88.2 & 75.0 \\ \hline \multirow{3}{*}{GAN-based methods} & IAGAT ([PERSON] et al., 2018)\({}^{*}\) & 0.615 & - & 80.8 & 50.3 \\  & DEGAT ([PERSON] et al., 2018)\({}^{*}\) & 0.777 & 241 & 87.1 & 72.4 \\  & T-GAE ([PERSON] et al., 2018)\({}^{*}\) & 0.696 & - & 86.2 & - \\ \hline \multirow{3}{*}{Language model-based methods} & StoKE ([PERSON] et al., 2018)\({}^{*}\) & 0.712 & - & 88.5 & 60.5 \\  & Llama-2-7b-CoH ([PERSON] et al., 2018)\({}^{*}\) & - & - & 69.9 & 38.6 \\ \cline{1-1}  & Vicuna-7b-CoH ([PERSON] et al., 2018)\({}^{*}\) & - & - & 70.7 & 39.2 \\ \hline \hline \end{tabular} We use different symbols to represent the setting information, where \({}^{*}\) represents the static filtered setting, \({}^{*}\) represents the raw setting, \({}^{*}\) represents the time-wise filtered setting, and \({}^{*}\) represents that the setting information is not given.  
\end{table} 
Table 3: Experimental results on ICEWS05-15. (3) The highest Hits@1 value of the methods under the time-wise filtered setting does not exceed 61.2% (HyGNet), indicating that there is a lot of room for improvement in all categories of deep learning-based TKGC methods. (4) The method ConvTKG under the raw setting has a higher Hits@1 value than the methods under the static and time-wise filtered settings. This reflects the effectiveness of CNN in TKGC to a certain extent; however, in order to make a fairer comparison, further verification is needed under the static and time-wise filtered settings. Similarly, methods that do not provide setting information also need further verification. (5) TA-TransE directly merges temporal information into relations, thereby converting TKGC into static KGC, and introduces LSTM to encode the merge information. This method has a simple structure, but the number of parameters increases and the characteristics of TKGC are not fully exploited, resulting in a Hits@1 value much lower than other methods. (6) The application of large language models, especially Llama and Vicuna, in TKGC is still in its initial exploratory stage, and its experimental results are lower than most other categories of deep learning-based methods. Large language models have huge parameters and require a lot of training data or clever fine-tuning strategies.