# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 4 Experimental Results and Analysis
In the experiment, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80% error facts were injected into the dataset. For example, adding a 10% error fact means that each of the three types of relationships added a 10% error fact. Table 2 gives the AUC of Kgedl under different noises. It can be seen that even if 80% of the error facts were added, the AUC value in Kgedl was still 0.89, which fully demonstrates that the proposed method is robust. Figure 2 shows the comparison of Kgedl with several baseline methods on the YAGO dataset. It can be seen that Kgedl was obviously superior to the baseline methods. When the recall rate was 5%, the accuracy rates of Kgedl and nPSL, TransR, and PRA were 0.91, 0.9, 0.89, and 0.88, respectively. When the recall rate was 50%, the accuracy rates of Kgedl and nPSL, TransR, and PRA were 0.73, 0.7, 0.68, 0.55, respectively, and when the recall rate was 90%, the accuracy of Kgedl and nPSL, TransR, and PRA were 0.25, 0.13, 0.1, and 0.08, respectively. The experimental results on the IMDB dataset are shown in Figure 3. The accuracy rates of Kgedl and nPSL, TransR, and PRA were 0.946, 0.927, 0.914, 0.905 when the recall rate was 5%. When the recall rate rose to 50%, the accuracy of Kgedl, nPSL, TransR, and PRA dropped to 0.809, 0.721, 0.698, 0.665, and when the recall rate rose to 90%, the accuracy of Kgedl, nPSL, TransR, and PRA dropped to 0.26, 0.133, 0.102, 0.08. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **\#Rel** & **\#Ent** & **\#Train** & **\#Valid** & **\#Test** & **\#Facts** \\ \hline YAGO & 12 & 10,712 & 29,403 & 3675 & 3676 & 36,754 \\ IMDB & 10 & 11,364 & 38,836 & 4854 & 4856 & 48,546 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: YAGO (Yet Another Great Ontology) and IMDB (Internet Movie Database) datasets statistics. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Noise** & **0\%** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** \\ \hline YAGO’AUC & 0.94 & 0.93 & 0.92 & 0.91 & 0.9 & 0.9 & 0.89 & 0.9 & 0.89 \\ IMDB’AUC & 0.95 & 0.94 & 0.94 & 0.93 & 0.92 & 0.91 & 0.91 & 0.9 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of Kgedl with different scale of noise data. Figure 4 compares the operating efficiency of Kgedl with the baseline methods, from which it can be seen that Kgedl had the best performance. On the YAGO dataset, Kgedl's runtime was 30% of nPSL's and 15% of TransR's. On the IMDB dataset, Kgedl's runtime was 27% of nPSL's and 14% of TransR's. Figure 4: The runtime comparison between Kgedl and three baseline methods on the YAGO and IMDB datasets. Figure 3: The recall rate precision comparison results between Kgedl and baseline methods on the IMDB dataset. Figure 2: The recall rate precision comparison results between Kgedl and baseline methods on the YAGO dataset. The effect of solving the knowledge conflict problem in the evolution process of the Kgedl knowledge graph has been discussed above, and the method was used to test its performance in link prediction in the following experiment. Given the test tuple \(<\)s, \(p\), \(o\), \(w\), [\(b\), \(e\)]\(>\), all entities in the YAGO dataset were replaced with the entity of the base facts and relationships with the target entity for all candidate entities in the tuple were generated. The probability was sorted in a descending order, and Mean Absolute Rank (MAR), which is the ranking of the entity corresponding to the link prediction in the candidate entity. The smaller MAR, the better the link prediction method. STAndard DEVIation (STDEV) for MAR and HITS@10 were evaluated in the experiment. Figure 5 shows the effect of Kgedl and the baseline methods on link prediction problem on the YAGO dataset. It can be seen that Kgedl was significantly better than those baseline methods. The MAR value obtained by Kgedl was 22 higher than the average of the second-ranked method nPSL.