GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science

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.

Automated summary

By merging existing GIS bodies of knowledge and applying deep-learning methods, researchers built a GIS knowledge graph (GIS-KG) to facilitate information retrieval. The experiments demonstrated the robust support and potential of GIS-KG in exploring emerging research themes.