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,...

Full description

Saved in:
Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2022-05, Vol.36 (5), p.873-897
Main Authors: Du, Jiaxin, Wang, Shaohua, Ye, Xinyue, Sinton, Diana S., Kemp, Karen
Format: Article
Language:English
Subjects:
Geographic information science (GIS)
information retrieval
knowledge graph
natural language processing
ontology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1365-8816
1362-3087
DOI:10.1080/13658816.2021.2005795