# Measuring the Inferential Values of Relations in Knowledge Graphs
## 5 Experimental
### Experimental Results and Analyses  
In this section, we explain the experimental results and analyze some of the potential symptoms and causes behind them. **Triple Classification. ** The PR curves and ROC curves of the triplet classification experiment are shown in Figures 5-7, respectively. The sizes of the four training sets obtained after removing the four groups of relations are shown in Table 3. The closer the PR curve is to the upper right corner and the ROC curve to the upper left corner, the better the experimental results are considered. The area ratios (%) under the PR curve and ROC curve (AUC) are shown in Tables 4-6. It can be seen that compared with the experimental results on the original training set \"Raw\", all the other three groups have shown varying degrees of declines. \"REntropy\" declines more than \"Random\"; this verifies the hypothesis that relations with higher relation entropy have greater inferential value. According to Table 3, the train dataset of \"REntropy\" is smaller than \"Random\", so we add \"Max\" to analyze the contact between the inferential value and the triples contained in the relation. Among these, the \"Max\" and \"REntropy\" experiments exhibited more significant decreases than others, and this indicates that these two sets of relations have higher reasoning value and greater impact on model training and learning. According to the hypothesis of this study, the experimental results should have the most significant decrease after removing the relations with high relation entropy. However, the \"Max\" experiment achieved results similar to those of the \"REntropy\" experiment. This is because the relations in \"Max\" are the top@k relations with the highest frequency in the dataset, and they cover a large number of triples. After these relations are removed, the amount of data in the knowledge graph drops sharply, and this results in a significant loss of knowledge graph structure and semantics. Thus, this leads to a \"qualitative change  
Figure 5: PR and ROC curves on datasets FB15K-237, NLL-995, YAGO3-10 by TransE. “**Raw**” represents the experimental result on the original training set with no relation removed. “**Random**” represents the experimental result on the training set formed after randomly removing a group of relations from the training set. “**Max**” represents the experimental result on the training set formed after removing the top@k most frequently occurring relations from the training set. “**REntropy**” represents the experimental result on the training set formed after removing the Top@k relation with the highest relation entropy from the training set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Raw** & **Random** & **Max** & **REntropy** \\ \hline FB15K-237 & 272,115 & 260,384 & 173,324 & 219,407 \\ NELL-995 & 149,678 & 118,658 & 82,329 & 94,227 \\ YAGO3-10 & 1,079,040 & 862,142 & 149,067 & 764,671 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of triples on different training sets in FB15K-237, NLL-995, YAGO3-10. It can also be seen that relation frequency is not the only determining factor affecting relation inferential value by data, as shown in Figure 8, where the numbers of triples in the training set obtained after removing the four groups of relations are displayed. * Comparing Figures 5-7 with Figure 8, it can be observed that the triples in the \"Random\" group are more than the other two groups, and the experimental results on \"Random\" exceed those on \"Max\" and \"REntropy\". * As listed in Tables 4-6, as well as the number of triples in Table 3, the triples in \"Max\" are less than in \"REntropy\", but its corresponding experimental results are better than or close to those of \"REntropy\". * Even in YAGO3-10, \"Max\" has only one-fifth of the triples of \"REntropy\", but its experimental results are better than \"REntropy\" and even better than \"Random\".