# Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space
## 4 Experiment and Analysis
### Experiments
#### 4.4.2 Entity Predication  
In this section, by link prediction, DMuRP is compared with the model fusing entity descriptions of Euclidean space to prove the effectiveness of our method. Specifically, it can prove that our fusion method and the balance factors are effective. In order to prove that the embeddings of hyperbolic space has certain advantages in low dimensions, we also give the experimental results in 40 dimensions. The comparison of experimental results between DMuRP and the models of Euclidean space is shown in Table 4. These models are fused with entity descriptions. The DKRL model is an early attempt to add description information to the knowledge graph. Most of the models described by fusion entities will be compared with it. Therefore, we use the DKRL model as our baseline. The characteristics of other comparison models are as follows. The DKRL [22] model uses CNN to encode the descriptions of entities, and learns the entity representations of description-based and structure-based through cross fusion. Jointly(LSTM) [35] model introduces LSTM to encode the descriptions of entities, and combines a gating mechanism to improve the experimental results. The SSP [36] model uses the semantic space projection which jointly learns from the symbolic triples and textual descriptions. It offers precise semantic embedding. The EHP [24] model learns both from knowledge triples and text descriptions, which maps entities of triples in knowledge space to the description space. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**WN18**} \\ \cline{2-3}  & **MR** & **Hit@10** \\ \hline DMuRP & **89.6** & **0.963** \\ \(\alpha_{2}=0\) & 94.2 & 0.962 \\ \(\alpha_{2}=0,\alpha_{4}=0\) & 104.3 & 0.962 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of balance factor on WN18. The EDGE [38] model proposes entity descriptions-guided embedding to learn the knowledge graph representations with semantic guidance from entity descriptions. The TransE_Transformer [37] model uses the complete attention mechanism and Transformer encoder to improve the experimental results in link prediction. The MCapsEED [23] model combines relation attention mechanism, Transformer and capsule network to better integrate the structured and description representations of entities into a synthetic representation. The Teger_ConvE [25] model proposes to model the whole auxiliary text corpus with a graph. It enriches the knowledge graph representations and alleviating the inherent structure sparsity. Under the same dimensions (d = 100) and the same dataset, DMuRP achieved better results. In order to illustrate the advantages of low-dimensional embeddings of knowledge graph in hyperbolic space, the experimental results of DMuRP in different dimensions are compared and analyzed. Overall, the DMuRP effect in hyperbolic space is more prominent, especially the MR value, which indicates that the representation learning in hyperbolic space is richer than that in Euclidean space. According to the analysis of the Poincare disk [40; 45], the distance between two points closer to the edge is infinite if the distance is calculated directly along the edge, which can be understood as the entity embeddings closer to the edge, the more space there is to distinguish the two entities semantic information. Hyperbolic space has more advantages in tree-like hierarchical structure data. Since WN18 dataset has hierarchical structure and FB15k has less hierarchical data, the MR of WN18 has a greater increase than that of FB15k. Specifically, for FB15k, Hit@10 performs poorly. We present the ranking distribution of FB15k's Hit@10, as shown in Figure 7. The higher the number of positive triples, the better. Hit@n the higher the better. Hit@n represents the proportion of positive triples ranking within the top \(n\) in the total triples. \(\Delta\)Hit@n indicates the proportion of positive triples ranking in the \(n\)-th.