@article{f8177df818a14620b57dcb67a9c3a37b,
title = "GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science",
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.",
keywords = "Geographic information science (GIS), information retrieval, knowledge graph, natural language processing, ontology",
author = "Jiaxin Du and Shaohua Wang and Xinyue Ye and Sinton, {Diana S.} and Karen Kemp",
note = "Funding Information: We greatly appreciate the helpful comments and suggestions from the editor and anonymous reviewers. The research was supported by National Science Foundation (NSF) under grants OIA-1937908 and SMA-2122054, Texas A&M University Harold Adams Interdisciplinary Professorship Research Fund, and College of Architecture Faculty Startup Fund. The funders had no role in the study design, data collection, analysis, or preparation of this article. Portions of this research were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing. Funding Information: This work was supported by the NSF [1937908, 2122054]; Texas A&M University Harold Adams Interdisciplinary Professorship Research Fund; Texas A&M University College of Architecture Faculty Startup Fund We greatly appreciate the helpful comments and suggestions from the editor and anonymous reviewers. The research was supported by National Science Foundation (NSF) under grants OIA-1937908 and SMA-2122054, Texas A&M University Harold Adams Interdisciplinary Professorship Research Fund, and College of Architecture Faculty Startup Fund. The funders had no role in the study design, data collection, analysis, or preparation of this article. Portions of this research were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing. Publisher Copyright: {\textcopyright} 2021 Informa UK Limited, trading as Taylor & Francis Group.",
year = "2022",
doi = "10.1080/13658816.2021.2005795",
language = "English (US)",
volume = "36",
pages = "873--897",
journal = "International Journal of Geographical Information Science",
issn = "1365-8816",
publisher = "Taylor and Francis Ltd.",
number = "5",
}