# Causal Reinforcement Learning for Knowledge Graph Reasoning
## 4 Experiments
### Analysis  
#### 4.6.1 Setting of Standard Values and Ablation Study  
We take the experimental results on the NELL-995 dataset as an example to study the influence of different standard values and further measure the influence of prior knowledge \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Tasks** & **TransE** & **PRA** & **DeepPath** & **MINERVA** & **Our Model** \\ \hline \multirow{8}{*}{NELL-995} & AthletePlaysInLeague & 77.3 & 84.1 & 92.7 & 95.2 & **95.4** \\  & AthletePlaysForTeam & 62.7 & 54.7 & 75.0 & 82.4 & **86.9** \\  & AthleteHomeStadium & 71.8 & 85.9 & 89.0 & 89.5 & **90.7** \\  & TeamPlaySports & 76.1 & 79.1 & 73.8 & 84.6 & **85.5** \\  & OrgHeadquaterCity & 62.0 & 81.1 & 79.0 & **94.5** & 93.6 \\  & BorLocation & 71.2 & 66.8 & 75.7 & 79.3 & **82.2** \\  & OrgHiredPerson & 71.9 & 59.9 & 74.2 & 85.1 & **86.4** \\  & PersonLeadsOrg & 75.1 & 68.1 & 75.5 & **83.0** & 81.4 \\  &... & & & & & \\ \cline{2-6}  & Overall & 71.0 & 72.5 & 79.4 & 86.7 & **87.8** \\ \hline \multirow{8}{*}{FB15k-237} & adjoins & **68.4** & 41.8 & 69.1 & 71.8 & 60.1 \\  & contains & **56.7** & 32.5 & 39.8 & 41.5 & 40.8 \\  & personNationality & 44.2 & 42.1 & 52.8 & **62.1** & 42.9 \\  & filmDirector & 41.5 & 32.8 & 45.6 & 38.9 & **46.3** \\  & filmLanguage & **61.5** & 45.1 & 52.5 & 58.9 & 46.1 \\  & filmWritten & 56.1 & 32.1 & 36.5 & **59.1** & 36.2 \\  & capitaLOf & 42.5 & 25.8 & 43.8 & 48.9 & **50.6** \\  & musicianOrigin & 38.2 & 18.5 & 23.7 & 23.8 & **38.7** \\  &... & & & & & \\ \cline{2-6}  & Overall & **45.3** & 31.5 & 39.8 & 42.3 & 45.2 \\ \hline \hline \end{tabular} Bold indicates the highest score.  
\end{table} 
Table 2: The MAP scores on NELL995 and FB15K-237. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Metric** & **TransE** & **CompLEX** & **ConvE** & **MINERVA** & **Our Model** \\ \hline \multirow{8}{*}{NELL-995} & Hit@1 & 0.514 & 0.612 & 0.672 & 0.663 & **0.684** \\  & Hit@3 & 0.678 & 0.761 & **0.808** & 0.773 & 0.797 \\  & Hit@10 & 0.751 & 0.827 & 0.864 & 0.831 & **0.871** \\  & MRR & 0.456 & 0.694 & **0.747** & 0.725 & 0.740 \\ \hline \multirow{8}{*}{FB15K-237} & Hit@1 & 0.248 & 0.303 & **0.313** & 0.217 & 0.228 \\  & Hit@3 & 0.401 & 0.434 & 0.457 & 0.329 & **0.464** \\  & Hit@10 & 0.450 & 0.572 & **0.600** & 0.456 & 0.491 \\  & MRR & 0.361 & 0.394 & 0.410 & 0.293 & **0.422** \\ \hline \multirow{8}{*}{WN18 RR} & Hit@1 & 0.289 & 0.382 & 0.403 & 0.413 & **0.440** \\  & Hit@3 & 0.475 & 0.433 & 0.452 & 0.456 & **0.485** \\ \cline{1-1}  & Hit@10 & 0.560 & 0.480 & 0.519 & 0.513 & **0.542** \\ \cline{1-1}  & MRR & 0.359 & 0.415 & 0.438 & 0.448 & **0.474** \\ \hline \multirow{8}{*}{YAGO3-10} & Hit@1 & 0.248 & 0.260 & 0.350 & 0.355 & **0.380** \\ \cline{1-1}  & Hit@3 & 0.412 & 0.405 & 0.490 & 0.498 & **0.510** \\ \cline{1-1}  & Hit@10 & 0.315 & 0.550 & 0.620 & 0.650 & **0.678** \\ \cline{1-1}  & MRR & 0.308 & 0.360 & 0.440 & 0.470 & **0.492** \\ \hline \end{tabular} Bold indicates best performance.  
\end{table} 
Table 3: Link prediction results (Hits@N and MRR) on datasets WN18 RR, FB15k-237, NELL-995 and YAGO3-10. introduced by causal inference. Since the maximum weight of most relationships on the NELL-995 dataset is 0.1, we chose three standard values of 0.01, 0.05, and 0.1 for the following experiment. Furthermore, to ensure the consistency of the experimental results, we still use the Hits@N and MRR indicators to measure the performance differences in this ablation study, where N is still 1, 3, and 10. The experimental results are illustrated in Figure 4.  
As depicted in Figure 4, after introducing the prior knowledge, the performance of the reinforcement-learning method is significantly improved, demonstrating our method's effectiveness. It should be noted that the method's performance is related to a standard value, as within a certain range, the larger the standard value, the better the method's performance, preserving the standard value between 0.01 and 0.05.