# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 6 Conclusions  
This paper proposes a multimodal geographic knowledge graph learning method called MGKGR. This method employs a two-stage fusion method to integrate the structural features of the geographic knowledge graph with the multimodal semantic information of geographic entities, effectively enhancing the representation of geographic knowledge graphs. Experimental results demonstrate that incorporating multimodal semantic information can significantly improve the quality of geographic knowledge graph representation. Furthermore, this paper verifies that adding additional semantic information to adjacency relationships can effectively enhance the model's capability. However, there are still some limitations in this study, such as not accounting for the missing modality data in some entities and determining the optimal ratio between attribute triples and adjacency triples. These issues will be further explored in future work.  
**Author Contributions:** Conceptualization, J.Z. and R.C.; Data curation, J.Z. and T.L.; Formal analysis, J.Z., R.C., S.L. and H.Y.; Investigation, J.Z.; Methodology, J.Z. and R.C.; Project administration, J.Z. and R.C.; Software, J.Z.; Supervision, J.Z., H.Y. and S.L.; Validation, J.Z. and R.C.; Visualization, J.Z.; Writing--original draft, J.Z., R.C. and S.L.; Writing--review and editing, J.Z., R.C. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**PA-30K**} & \multicolumn{5}{c}{**FL-25K**} \\ \cline{3-10}  & & **Hit@1** & **Hit@3** & **Hit@10** & **MRR** & **Hit@1** & **Hit@3** & **Hit@10** & **MRR** \\ \hline \multirow{2}{*}{TransE} & “Rich” & 30.66 & 48.37 & 50.58 & 39.51 & 33.51 & 49.83 & 52.08 & 41.82 \\  & “Sparse” & 30.47 & 48.34 & 50.55 & 39.37 & 33.14 & 49.38 & 50.94 & 40.15 \\ \hline \multirow{2}{*}{DistMult} & “Rich” & 23.81 & 32.48 & 34.67 & 28.52 & 33.06 & 42.36 & 45.17 & 38.46 \\  & “Sparse” & 28.41 & 35.25 & 38.56 & 33.64 & 44.05 & 45.98 & 49.38 & 38.58 \\ \hline \multirow{2}{*}{RGCN} & “Rich” & 32.22 & 46.99 & 49.89 & 39.93 & 35.36 & 47.62 & 49.82 & 41.83 \\  & “Sparse” & 31.78 & 44.99 & 49.37 & 39.54 & 34.76 & 47.24 & 50.61 & 40.54 \\ \hline \multirow{2}{*}{MKGFormer} & “Rich” & 34.04 & 48.75 & 51.72 & 41.85 & 36.24 & 48.93 & 51.14 & 42.99 \\  & “Sparse” & 31.51 & 47.22 & 51.43 & 40.56 & 36.39 & 48.94 & 50.88 & 40.44 \\ \hline \multirow{2}{*}{MGKGR (ours)} & “Rich” & **35.06** & **49.93** & **52.43** & **42.96** & **36.63** & **50.16** & 52.73 & **43.92** \\  & “Sparse” & 34.98 & 49.89 & 52.22 & 42.89 & 36.26 & 50.11 & **52.83** & 43.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison on different diversity of relation semantics. The bold indicates the highest value in the experiment.  
Data Availability Statement: The original data presented in this study are openly available from the [[https://github.com/zhang-jian-qiang/MGKKGR](https://github.com/zhang-jian-qiang/MGKKGR)]([https://github.com/zhang-jian-qiang/MGKKGR](https://github.com/zhang-jian-qiang/MGKKGR)), accessed on 30 October 2024.  
The authors declare no conflicts of interest.