# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 5 Discussion
### Performance Comparison Across Different Relation Scenarios  
In this paper, the triples in multimodal GeoKG are divided into attribute relation triples and adjacency relation triples. In the previous experiment, we focused on inferring attribute relations and did not evaluate the model in the adjacency relation scenario. This is based on the assumption that predicting geographic attributes holds greater significance in a geographic knowledge graph. To verify that our method also performs well in adjacency scenarios, we conducted experiments under different scenarios in this section and provided an analysis and comparison. Specifically, one-fifth of adjacency triples from the training set were included in the test set. To prevent information leakage, these adjacency triples were removed from the training set. During testing, adjacency triples and attribute triples were evaluated together. The results were separately recorded for adjacency scenarios, attribute scenarios, and mixed scenarios. The experiments were conducted using MGKGR and MKGFormer across two datasets. The results are shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**PA-30K**} & \multicolumn{5}{c}{**FL-25K**} \\ \cline{2-9}  & **Hit@1** & **Hit@3** & **Hit@10** & **MRR** & **Hit@1** & **Hit@3** & **Hit@10** & **MRR** \\ \hline MGKGR & 35.06 & **49.93** & **52.43** & 42.96 & 36.63 & **50.16** & 52.73 & **43.92** \\ \hline w/o Multimodal & 32.22 & 46.99 & 49.89 & 39.93 & 35.36 & 47.72 & 49.82 & 41.83 \\ w/o Structure & 35.00 & 49.15 & 51.55 & 42.38 & **37.03** & 49.87 & 51.61 & 43.76 \\ w/o CL & 35.26 & 49.86 & 52.34 & 43.04 & 35.97 & 49.62 & **52.98** & 43.49 \\ w/o SFF & **35.72** & 49.53 & 52.21 & 43.15 & 36.91 & 49.95 & 52.21 & 43.91 \\ w/o CL \& SFF & 35.41 & 49.88 & 52.38 & **43.16** & 34.41 & 48.71 & 52.71 & 43.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation Study of MGKGR. The bold indicates the highest value in the experiment.  
The experimental results indicate that both models achieve significantly higher prediction scores in adjacency scenarios compared to attribute scenarios. This supports the assumption that the prediction of geographic attributes holds greater significance within a geographic knowledge graph. Furthermore, MGKGR consistently outperforms MKG-Former across various metrics, particularly in predicting adjacency scenarios, thereby validating the effectiveness of our method in integrating multimodal semantic features with the structural features of geographic knowledge graphs. In comparing the experimental outcomes across two different datasets, the prediction results in adjacency scenarios are higher in the PA-30K dataset than in the FL-25K dataset, while results in attribute scenarios are comparatively lower in PA-30K. This discrepancy is ascribed to the higher proportion of adjacency triples in the PA-30K dataset, where excessive adjacency information can lead to biased learning within the model. Furthermore, compared to the experimental results in Section IV under attribute scenarios, the outcomes in this section are relatively lower. This discrepancy arises from the splitting of the dataset, where certain adjacency triples were moved from the training set to the test set, reducing the integrity of adjacency information in the training data and consequently weakening the model's predictive performance.