# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 4 Conclusions  
Geospatial knowledge graphs contain rich geographic location semantics, requiring graph partitioning algorithms to comprehensively consider both spatial relationships and graph structures. Traditional graph partitioning methods discard the spatial relationship between entities, resulting in poor performance for spatial queries. To solve this problem, we propose GKGP-DC to produce spatially aware partitions. By using density peak clustering to aggregate spatial entities to super-nodes, GKGP-DC avoids dividing close spatial entities into different partitions. Then, the Leiden algorithm is applied on the coarse graph, producing clusters which meet the requirement of minimizing cut edges. Finally, the LDG algorithm is used to refine the partitioning result, ensuring load balance without altering the clusters. Therefore, the final partitioning result can be obtained under three constraints: spatial relationship, graph structure, and load balance. Experiments on a geospatial knowledge graph (i.e., GeoKG) show that compared to ten representative methods, GKGP-DC can effectively reduce the number of computational node resources required for spatial queries and obviously improve the response time for spatial-graph coupled queries. GKGP-DC can be further used for the distributed management of large-scale geospatial knowledge graphs.  
Future studies should focus on these two aspects. First, GKGP-DC lacks the ability of processing spatial relationships between entities with different geometric types, such as points and polygons. GKGP-DC treats all geographic entities as points. The centroids of entities were used to represent the locations of entities. It is possible that a point entity is in a polygon entity, but the distance between the point and the centroid of the polygon is large. In this situation, the point may be assigned to a different partition, resulting in cross-partition communication and the low efficiency of spatial queries. When searching the \(k\)-nearest neighbors for DPC, taking the spatial relationship into account may address this issue. For example, if a polygon entity contains a point entity, they should always be considered as neighbors. In addition, GKGP-DC can only partition geospatial knowledge graphs based on the spatial relationship and graph structure. In practice, user preference also should be considered to partition the graph. One way to alleviate this problem is to adjust the partition based on the history records of users. We leave this issue for further research.  
Conceptualization, Y.C. and K.C.; data curation, F.O., Y.C. and M.C.; methodology, Y.C., K.C. and Q.L.; visualization, Y.C. and F.O.; writing--original draft, Y.C., K.C. and G.W.; writing--review and editing, Q.L., M.D. and R.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded through support from the National Key Research and Development Program of China (No. 2021 YFB3900903), the National Natural Science Foundation of China (No. 42271484), the water conservancy science and technology project of Guizhou, China (No. KT202410), and the Research Foundation of the Department of Natural Resources of Hunan Province (Grant No. HBZ20240132).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author(s).  
We deeply appreciate the anonymous reviewers for their constructive and helpful suggestions.  
[PERSON] was employed by the company Guizhou Survey & Design Research Institute for Water Resources and Hydropower. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.