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

被引：12
|
作者：
Du, Jiaxin [1 ]
Wang, Shaohua [2 ]
Ye, Xinyue [1 ]
Sinton, Diana S. [3 ,4 ]
Kemp, Karen [5 ]
机构：
[1] Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX 77843 USA
[2] New Jersey Inst Technol, Ying Wu Coll Comp, Newark, NJ 07102 USA
[3] Univ Consortium Geog Informat Sci, Ithaca, NY 14851 USA
[4] Cornell Univ, Coll Agr & Life Sci, Ithaca, NY 14853 USA
[5] Univ Southern Calif, Dornsife Coll Letters Arts & Sci, Los Angeles, CA USA
来源：
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE | 2022年 / 36卷 / 05期
关键词：
Geographic information science (GIS); ontology; knowledge graph; information retrieval; natural language processing; QUEST; MARK; WEB;
D O I：
10.1080/13658816.2021.2005795
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：873 / 897
页数：25
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