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

Jiaxin Du, Shaohua Wang, Xinyue Ye, Diana S. Sinton, Karen Kemp

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)873-897
Number of pages25
JournalInternational Journal of Geographical Information Science
Volume36
Issue number5
DOIs
StatePublished - 2022

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

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