Abstract
In this paper, we propose the fine-grained geospatial knowledge graph (FineGeoKG), which can capture the neighboring relations between geospatial objects. We call such neighboring relations strong geospatial relations (SGRs) and define six types of SGRs. In FineGeoKG, the vertices (or entities) are geospatial objects. The edges (or relations) can have “sgr” labels together with properties, which are used to quantify SGRs in both topological and directional aspects. FineGeoKG is different from WorldKG, Yago2Geo, and other existing geospatial knowledge graphs, since its edges can capture the spatial coherence among geospatial objects. To construct FineGeoKG efficiently, the crucial problem is to find out SGRs. We improve the existing geospatial interlinking algorithm in order to find out SGRs faster. We conduct experiments on the real datasets and the experimental results show that the proposed algorithm is more efficient than the baseline algorithms. We also demonstrate the usefulness of FineGeoKG by presenting the results of complicated spatial queries which focus on structural and semantic information. Such queries can help researchers (for example, ecologists) find groups of objects following specific spatial patterns.
Supported by Beijing Key Laboratory of Knowledge Engineering for Materials, State Key Laboratory of Precision Blasting (Jianghan University), and Key Laboratory of AI and Information Processing (Hechi University).
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Notes
- 1.
The angles \(\alpha _s\) and \(\alpha _e\) indicate the directions of the endpoints on the shared border.
- 2.
The intersection line is the straight line passing through the intersections of \(o_x\) and \(o_y\).
- 3.
A polyline consists of a group of consecutive segments. A segment of \(o_x\) and a segment of \(o_y\) intersect at a point.
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Acknowledgments
This work was supported by Key Laboratory of AI and Information Processing (Hechi University) Education Department of Guangxi Zhuang Autonomous Region (2022GXZDSY007), National Natural Science Foundation of China (No. 61602031), Fundamental Research Funds for the Central Universities (FRF-IDRY-19-023).
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Wei, B., Guo, X., Wu, Z., Zhao, J., Zou, Q. (2023). Construct Fine-Grained Geospatial Knowledge Graph. In: El Abbadi, A., et al. Database Systems for Advanced Applications. DASFAA 2023 International Workshops. DASFAA 2023. Lecture Notes in Computer Science, vol 13922. Springer, Cham. https://doi.org/10.1007/978-3-031-35415-1_19
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