"GIS-KG: building a large-scale hierarchical knowledge graph for geogra" by Jiaxin Du, Shaohua Wang et al.
 

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

Document Type

Article

Publication Date

1-1-2022

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.

Identifier

85119981012 (Scopus)

Publication Title

International Journal of Geographical Information Science

External Full Text Location

https://doi.org/10.1080/13658816.2021.2005795

e-ISSN

13623087

ISSN

13658816

First Page

873

Last Page

897

Issue

5

Volume

36

Grant

SMA-2122054

Fund Ref

Texas A and M University

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