Abstract
An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 873-897 |
| Number of pages | 25 |
| Journal | International Journal of Geographical Information Science |
| Volume | 36 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2022 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Information Systems
- Geography, Planning and Development
- Library and Information Sciences
Keywords
- Geographic information science (GIS)
- information retrieval
- knowledge graph
- natural language processing
- ontology