# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 5 Discussion  
### Ablation Study  
This section discusses the impact of each module on the overall model performance. The MGKGR captures structural features from the GeoKG and fuses them with multimodal semantic features through a two-stage multimodal feature fusion process. It further constrains the distribution between semantic and structural features through contrastive learning, thereby achieving favorable results. Therefore, the ablation experiments are conducted under the following settings:  
* w/o Multimodal: MGKGR without the use of multimodal data. The input data are \(G_{\text{geo}}\) defined in Section 3.1 excluding the multimodal dataset \(M\).
* w/o Structure: MGKGR without the fusion of structural features captured by KRL model.
* w/o CL: MGKGR without the contrastive learning.
* w/o CL and SFF: MGKGR without both the contrastive learning and the spatial feature fusion for modality enhancement.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**PA-30K**} & \multicolumn{4}{c}{**FL-25K**} \\ \cline{2-9}  & **Hit@1** & **Hit@3** & **Hit@10** & **MRR** & **Hit@1** & **Hit@3** & **Hit@10** & **MRR** \\ \hline TransE & 30.66 & 48.37 & 50.58 & 39.51 & 33.51 & 49.88 & 52.08 & 41.82 \\ DistMult & 23.81 & 32.48 & 34.67 & 28.52 & 33.06 & 43.26 & 45.17 & 38.46 \\ ComplEx & 32.04 & 43.75 & 45.33 & 38.12 & 31.83 & 44.12 & 45.62 & 38.13 \\ ConvE & 19.49 & 26.49 & 30.46 & 24.08 & 20.18 & 25.88 & 29.87 & 24.08 \\ CompGCN & 18.89 & 24.89 & 25.84 & 22.11 & 19.83 & 25.40 & 26.46 & 22.88 \\ RGCN & 32.22 & 46.99 & 49.89 & 39.93 & 35.36 & 47.72 & 49.82 & 41.83 \\ MKGFormer & 34.04 & 48.75 & 51.72 & 41.85 & 36.24 & 48.93 & 51.14 & 42.99 \\ \hline MGKGR (ours) & **35.06** & **49.93** & **52.43** & **42.96** & **36.63** & **50.16** & **52.73** & **43.92** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall results of MGKGR and baselines. The bold indicates the highest value in the experiment, and the underlined values represent the second highest.  
The experiments were conducted on the two datasets we constructed, and the results are presented in Table 3. Considering all metrics across the two datasets, MGKGR achieved excellent overall performance. Notably, compared to the outcomes without using multimodal data (w/o Multimodal), it is evident that multimodal semantic features significantly enhance the feature representation of geographic knowledge graphs, thereby improving the model's performance. This indicates that, in scenarios similar to the example shown in Figure 1, combining multimodal semantic information from text and images can effectively improve the model's ability to predict geographic entity attributes. In the PA-30K dataset, the ablation of the SFF and contrastive learning mechanisms leads to improvements in certain metrics, which can be attributed to the relatively dense distribution of geographic entities and the substantial proportion of adjacency triples present. The fusion of adjacency and attribute features within the geographic knowledge graph may introduce model bias during training, potentially interfering with the precise prediction of attribute triples. However, the abundance of adjacency triples enriches the information contained within the geographic knowledge graph's features, resulting in enhanced overall performance and comparatively higher Hit@3 and Hit@10 metrics. Conversely, the FL-25K dataset has a sparser distribution of geographic entities, resulting in fewer adjacency triples. This prevents the model from developing a bias due to the large number of adjacency triples during the training process. Consequently, the model demonstrates improved performance in average rank predictions, yielding a higher MRR score.