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

页码：873 / 897

页数：25

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