HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning
The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient cover...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/1/18 |
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| author | Tailong Li Renyao Chen Yilin Duan Hong Yao Shengwen Li Xinchuan Li |
| author_facet | Tailong Li Renyao Chen Yilin Duan Hong Yao Shengwen Li Xinchuan Li |
| author_sort | Tailong Li |
| collection | DOAJ |
| description | The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective use and representation of semantic content. This paper presents HGeoKG, a hierarchical geographic knowledge graph that comprehensively models hierarchical structures, attributes, and spatial relationships of multi-type geographic entities. Based on the concept and construction methods of HGeoKG, this paper developed a dataset named HGeoKG-MHT-670K. Statistical analysis reveals significant regional heterogeneity and long-tail distribution patterns in HGeoKG-MHT-670K. Furthermore, extensive geographic knowledge reasoning experiments on HGeoKG-MHT-670K show that most knowledge graph embedding (KGE) models fail to achieve satisfactory performance. This suggests the need to accommodate spatial heterogeneity across different regions and improve the embedding quality of long-tail geographic entities. HGeoKG serves as both a reference for GeoKG construction and a benchmark for geographic knowledge reasoning, driving the development of geographical artificial intelligence (GeoAI). |
| format | Article |
| id | doaj-art-cc417ec99d8040c6b99b434299e3adc9 |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-cc417ec99d8040c6b99b434299e3adc92025-01-24T13:34:59ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-01-011411810.3390/ijgi14010018HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge ReasoningTailong Li0Renyao Chen1Yilin Duan2Hong Yao3Shengwen Li4Xinchuan Li5School of Future Technology, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Future Technology, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaThe Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective use and representation of semantic content. This paper presents HGeoKG, a hierarchical geographic knowledge graph that comprehensively models hierarchical structures, attributes, and spatial relationships of multi-type geographic entities. Based on the concept and construction methods of HGeoKG, this paper developed a dataset named HGeoKG-MHT-670K. Statistical analysis reveals significant regional heterogeneity and long-tail distribution patterns in HGeoKG-MHT-670K. Furthermore, extensive geographic knowledge reasoning experiments on HGeoKG-MHT-670K show that most knowledge graph embedding (KGE) models fail to achieve satisfactory performance. This suggests the need to accommodate spatial heterogeneity across different regions and improve the embedding quality of long-tail geographic entities. HGeoKG serves as both a reference for GeoKG construction and a benchmark for geographic knowledge reasoning, driving the development of geographical artificial intelligence (GeoAI).https://www.mdpi.com/2220-9964/14/1/18geographic knowledge graphknowledge reasoninghierarchical structurelong-tail distributionspatial heterogeneity |
| spellingShingle | Tailong Li Renyao Chen Yilin Duan Hong Yao Shengwen Li Xinchuan Li HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning ISPRS International Journal of Geo-Information geographic knowledge graph knowledge reasoning hierarchical structure long-tail distribution spatial heterogeneity |
| title | HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning |
| title_full | HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning |
| title_fullStr | HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning |
| title_full_unstemmed | HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning |
| title_short | HGeoKG: A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning |
| title_sort | hgeokg a hierarchical geographic knowledge graph for geographic knowledge reasoning |
| topic | geographic knowledge graph knowledge reasoning hierarchical structure long-tail distribution spatial heterogeneity |
| url | https://www.mdpi.com/2220-9964/14/1/18 |
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