# Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs
## 3 Experiment Result and Analysis
### Experimental Comparison and Analysis
#### 3.2.2 Ablation Studies  
To investigate the impact of different model components on the performance of SS-DGA, this section conducted a set of ablation experiments on Dataset 4. These experiments explored the impact of each enhancement by removing them one at a time. Initially, for the SS-DGA module, which builds upon the acquisition of semantic information, the directed graph representation model of the patent knowledge graph designed in this paper was removed, abbreviated as DS. Subsequently, the effects of integrating graph structural information and the attention mechanism were analyzed and were denoted as SI and AM, respectively. Furthermore, to further demonstrate the effects of different components within SS-DGA, the performance of the baseline and SS-DGA (with various model components removed) was compared. Table 5 displays the performance comparison results of the ablation experiments.  
By conducting ablation studies on certain components of the SS-DGA, it was found that each component of the model impacts performance, demonstrating that all parts of the model are effective. Specifically, in Dataset 4, the removal of the directed graph representation model led to a decline in MRR performance by 2.4 percentage points and a decrease in Hits@3 by 1.6 percentage points. Removing the directed graph model means that, specifically within the patent domain, the model cannot intuitively display the structure and relationships of patents, which diminishes the convenience of information extraction. Without the help of a patent-specific directed graph model, it is impossible to obtain more direct neighbor information around entities, leading to an overall decrease in model performance. This proves that neighborhood information of entities can indeed better enrich  
Figure 8: Boxplot of comparative experimental results between SS-DGA and baseline models on Dataset 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods of Comparison**} & \multicolumn{3}{c}{**Evaluation Indicators**} \\ \cline{2-4}  & **MR** & **MRR** & **Hits@3** \\ \hline ComplEx & 3234 & 0.398 & 0.401 \\ \hline RotatE & 2142 & 0.349 & 0.315 \\ \hline MLMLM & 3452 & 0.317 & 0.414 \\ \hline DensE & 1780 & 0.421 & 0.421 \\ \hline SS-DGA (w/0\_DS) & 2970 & 0.518 & 0.496 \\ \hline SS-DGA (w/0\_SI) & 2120 & 0.488 & 0.486 \\ \hline SS-DGA (w/0\_AM) & 1805 & 0.509 & 0.501 \\ \hline SS-DGA & **1521** & **0.542** & **0.512** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of SS-DGA Component Ablation Experiments.  
entity representations and also provide more semantic representations for reasoning tasks. In experiments where the module integrating graph structure was removed, the MRR and Hits@3 indices decreased by 5.4 and 2.6 percentage points, respectively. The significant performance deterioration demonstrates the substantial impact of structural information on knowledge graph reasoning. Integrating structural information based on semantic information significantly enhances the overall performance of the model. Considering that the attention mechanism can adaptively enhance the expression of important features in the model, the effects of having or lacking this mechanism were also tested. The experiments showed that the removal of the attention mechanism resulted in decreases in MRR and Hits@3 by 3.3 and 1.1 percentage points, respectively. The attention mechanism allows the model to focus more intensively on analyzing and extracting features from critical areas, thereby better capturing key information to enhance the model's reasoning capabilities. In summary, the ablation experiments verified that each part of the model contributes significantly to final performance, demonstrating that the components of the model are not redundant or unnecessary.