# EDET: Entity Descriptor Encoder of Transformer for Multi-Modal Knowledge Graph in Scene Parsing
## 4 Experimental Results
### Validation of Attributes Selection Conditions  
To verify the effectiveness of our selection methods, we chose four entities in the industrial dataset: machine door, safety door, spindle, manipulator, and using the proposed attributes selection conditions to create a multi-modal graph. The entities and their selected attribute information are shown in Table 3, and the final multi-modal graph can be generated as shown in Figure 7.  
After obtaining node representations in the experiment, the node representation vector of each entity was compressed into the same two-dimensional space, and the distance between two representations shows the similarity of the two nodes. Four experiments were performed, including all attributes, without the _Has Part_ attribute, without the _Type_ attribute, and without the _Image_ attribute. The experimental results, respectively, were expressed in dark red, pink, blue, and green nodes, and different entity nodes with different shape nodes. Machine door, spindle, safety door, and manipulator were marked as triangle, square,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity Attribute** & **Has Part** & **Type** & **Image** \\ \hline Machine Door & plug & Parts & ✓ \\ Spindle & bearing & Parts & ✓ \\ Safety Door & plug & Equipment & ✓ \\ Manipulator & jaws & Parts & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected attributes of entities in industrial dataset.  
Figure 7: Multi-modal knowledge graph extracted from industrial data by attribute selection methods.  
rhombus, and circle, respectively. The results of the final attribute selection conditional validity experiment are shown in Figure 8.  
In Figure 8, it can be seen that the similarity between the node representations obtained by using all attribute aggregation is not much different. When the _Type_ attribute is masked, the similarity between machine door and safety door node representation increases; when the _Has Part_ attribute is masked, the similarity of machine door, spindle, and manipulator node representations increases; when the sub-image information of entity nodes is masked, the similarity between all node representations is further improved.  
Based on the results obtained by the aggregation of all attributes, the reason for the change of node representations was analyzed. Combining the attribute information, it can be seen that, when _Type_ is ignored, the node representations of the same _Has Part_ attribute become closer. Similarly, when ignoring the _Has Part_ attribute, nodes of the _Parts_ type become more similar. Compared with the other two attributes, the images contain too much redundant information, such as background information, and the similarity between all nodes is improved after the image attribute is ignored.  
Experimental results show that attribute selection conditions can assist in judging which attributes are key attributes among entities, and the selected attributes can preserve the independence and separability of entity nodes. This has certain research significance for the large-scale development of knowledge graphs and the development of models based on knowledge graphs.