# Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space
## 4 Experiment and Analysis
### Experiments
#### 4.4.1 Link Prediction Based on Entity Descriptions (Ablation Experiment)  
In this section, through ablation research, we compared and analyzed the experimental results of four energy functions \(E_{1}\), \(E_{2}\), \(E_{3}\) and \(E_{4}\), so that the energy function with good performance accounts for a larger proportion in the score function and the energy function with poor performance accounts for a smaller proportion in the score function. According to the performance of these four energy functions, it can also provide reference for us to set the value of balance factors. We set up the comparative experiments of DMuRPss (\(\Phi_{1}\)), DMuRPds (\(\Phi_{2}\)), DMuRPsd (\(\Phi_{3}\)), DMuRPdd (\(\Phi_{4}\)), and uses DMuRPs (without containing entity descriptions) as the baseline to compare and analyze the two representation effects of entity between description-based representations and structure-based representations. The hyperparameters of WN18 and FB15k are set, the learning rate lr = 60, the batchSize = 256, the dimensions d = 100 and the experimental results are taken after 500 iterations. The experimental results are shown in Table 2, Figures 5 and 6. (1) By comparing the two datasets horizontally, with using DMuRPss as the benchmark, the method of containing entity descriptions (DMuRPds, DMuRPsd, DMuRPdd) performed more prominently on FB15k, and generally performed on WN18. The main reason is that the length of entity descriptions in the two datasets is different. The descriptions of entities contain an average of 13 words in WN18, and entity descriptions in FB15k are an average of 69 words. Compared with FB15k, the semantic information of WN18 is relatively scarce. Therefore, it can be seen from Figure 5 and Figure 6 that the increase in WN18 is not as large as that of FB15k. In specific applications, the effect can be further improved by improving the quality and quantity of entity descriptions. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**WN18**} & \multicolumn{3}{c}{**FB15k**} \\ \cline{2-7}  & **MR** & **MRR** & **Hit@10** & **MR** & **MRR** & **Hit@10** \\ \hline DMuRPss & 123.0 & 0.901 & 0.958 & 38.7 & 0.561 & 0.772 \\ DMuRPds & 122.4 & 0.784 & 0.953 & 34.1 & 0.584 & 0.812 \\ DMuRPsd & 140.7 & 0.903 & 0.955 & 39.6 & 0.554 & 0.793 \\ DMuRPdd & 141.2 & 0.632 & 0.931 & 39.3 & 0.568 & 0.786 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Entity prediction results based on entity descriptions(2) By vertical comparison, according to the experimental results of FB15k, it can be seen that the method of containing entity descriptions (DMuRPs, DMuRPsd, DMuRPsd) is better than baseline DMuRPss, and it is also indicating that description-based representations contain more semantic information. In link prediction, description-based representations' results outperform structure-based representations' results. On the contrary, for WN18, the energy function of the fused entity description is relatively general, which indicates that the descriptions of the entity is relatively scarce. (3) From the specific analysis, it can be seen from Table 2 that the MR value of WN18 is not ideal. Through the analysis, it may be because the MR value is related to entity descriptions and the number of relations. Through the analysis, it may be because the MR value is related to relations and entity descriptions. From the overall observation of Figure 5, the effect of adding entity descriptions is not ideal, which may be because entity descriptions of WN18 are relatively less, but the performance of DMuRPsd is slightly better. Based on \(E_{1}\), it is found by comparing the formula of \(E_{2}\), \(E_{3}\) and \(E_{4}\), the experimental results may be affected by relations. Compared with FB15k, WN18 has fewer relations and less entity descriptions, and its performance will be poor, which will affect the experimental results. Figure 5: The variation of WN18 Hit@10 with the number of iterations. Figure 6: The variation of FB15k Hit@10 with the number of iterations.