HGeoKG
A Hierarchical Geographic Knowledge Graph for Geographic Knowledge Reasoning
Bibliographic Data
| ID | 22032962 |
|---|---|
| Authors | Tailong Li (0009-0002-6133-7427, China University of Geosciences), Renyao Chen (0000-0003-0741-8648, China University of Geosciences), Yilin Duan (0009-0001-1940-9581, China University of Geosciences), Hong Yao (0000-0002-7161-8639, China University of Geosciences), Shengwen Li (0000-0002-1829-4006, China University of Geosciences), Xinchuan Li (0000-0002-9465-0965, China University of Geosciences, corresponding author) |
| Year | 2025 |
| Volume | 14 |
| Issue | 1 |
| Pages | 18 |
| Publication date | 2025-01-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14010018 |
| OpenAlex | W4406049090 |
| Language | EN |
| Citations received | 1 |
| References cited | 29 |
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)
Cartography · Data mining · Data science · Embedding · Geography · Geospatial analysis · Graph · Information retrieval · Advanced Graph Neural Networks · Computer Science · Geographic Information Systems Studies · Topic Modeling · Artificial Intelligence · Theoretical Computer Science
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |