# EDET: Entity Descriptor Encoder of Transformer for Multi-Modal Knowledge Graph in Scene Parsing
## 4 Experimental Results
### Validation of Entity Descriptor Representation Methods  
After verifying the effectiveness of feature engineering, we proposed the idea of the Entity Descriptor. In order to verify that the idea of aggregating entity node information through the Entity Descriptor is feasible, preliminary experiments were performed on entity descriptors to verify its feasibility in simple tasks of a single-modal knowledge graph, and then the computer vision task and multi-modal knowledge graph were popularized in the subsequent experiments. We chose to verify the validity on the scientific publication dataset Cora. The Cora dataset contains 2708 scientific publications, 5429 edges, and a total of seven categories. Treating publications as entity nodes, each entity node consists of 1433 attributes, each representing a keyword, taking values represented by 0/1 only, corresponding to the absence/existence of that keyword. First, the publications were defined as entity nodes, while keywords were defined as entity properties and reference relationships were defined as relationship edges, and the reference network was converted into a single-modal knowledge graph. Secondly, the Entity Descriptor was used to encode entity nodes. The 1433 keyword attributes constitute a word list of attribute information through the embedding layer to generate 1433 one-dimensional vectors, and the dimension of this attributes vector was set to 128. Then, the initial encoding of each publication was converted to a two-dimensional vector of 1433 \(\times\) 128, using the aggregation function in Equation (4) to encode the entity node \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Params** & **mAcc** & **mIoU** & **Score** \\ \hline FCN [19] & 35,322,218 & 67.3 & 22.7 & 45.00 \\ FCN + Edge & 35,372,394 + 9 & **70.0** & **27.6** & **48.80** \\ PSPNet [20] & 46,582,337 & 68.9 & 28.4 & 48.65 \\ PSPNet + Edge & 46,632,513 + 9 & **76.6** & **39.0** & **57.80** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter comparison table. (Params: Number of training parameters + number of fixed parameters, mAcc: Average class accuracy, mIoU: Mean Intersection-over-Union, Score: Average of mAcc and mIoU). Figure 5: The effect of a small number of parameters on the training process. representation. Finally, the encoding was trained to the same GCN network as the reference network for 100 epochs. In the traditional GCN approach, the information propagation properties of the publication entities are used to obtain a randomness representation and then the classification results are obtained by a two-layer graph convolution network. Entity Descriptors, on the other hand, first embed attributes into high dimensions to obtain semantic representations of the attributes and then fuse the attribute information into the corresponding entity nodes by trainable weights. Experimental results can be seen in Table 2 and Figure 6. In the process of generating node representation, the node representation obtained by the traditional method is influenced by other adjacent nodes, where the trainable weight is between all nodes in the whole graph; the node representation obtained by the Entity Descriptor is only related to the properties of the entity node itself, and its trainable weight is only effective within the entity. This leads to the effect of the first epoch in Table 2 and Figure 6. The node representation obtained by the traditional method at the beginning has strong randomness and the classification accuracy is only 8%, while the Entity Descriptor already has a certain classification effect at the initial time, with a classification accuracy of 37%, far more than that of the traditional method. After the training of the same model and the same iters (100 epochs), the model classification accuracy of the traditional method was 77%, while that of the Entity Descriptor representation method was 82%, and the performance was improved by 6.49 percentage points.