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)
• ontology
• knowledge graph
• information retrieval
• natural language processing

Acknowledgements

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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The code and data that support the findings of this study are available from https://github.com/UrbanDS/GIS-KG.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

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

Notes on contributors

Jiaxin Du

Jiaxin Du is a Ph.D. student at the Department of Landscape Architecture and Urban Planning at Texas A&M University. He holds M.S. and B.S. in Geographical Information Systems from Zhejiang University. His research focuses on geospatial artificial intelligence, natural language processing, and their application in urban planning.

Shaohua Wang

Shaohua Wang is an assistant professor in the Department of Informatics, College of Computing, New Jersey Institute of Technology. His research interests include software engineering, program analysis, and artificial intelligence. Dr. Wang has published research on top computer science conferences and journals, such as ICSE, FSE, ASE, OOPSLA and TSC.

Xinyue Ye

Xinyue Ye is a Harold Adams Endowed Associate Professor at the Department of Landscape Architecture and Urban Planning at Texas A&M University. He holds a Ph.D. degree in Geographic Information Science from the Joint Program between University of California at Santa Barbara and San Diego State University, a M.S. in Geographic Information Systems from Eastern Michigan University, and a M.A. in Human Geography from University of Wisconsin at Milwaukee. His research focuses on geospatial artificial intelligence, smart cities, spatial econometrics, and urban computing.

Diana S. Sinton

Diana S. Sinton focuses on the teaching and learning of geographic information science and systems (GIS), especially in the natural and environmental sciences. She serves as a Senior Research Fellow for the University Consortium for Geographic Information Science (UCGIS), a non-profit scientific and educational organization that supports a community of practice around GIScience research and teaching in higher education. Sinton teaches courses in GIS and spatial analysis at Cornell University in Ithaca, New York.

Karen Kemp

Karen Kemp is Professor Emerita of the Practice of Spatial Sciences in the Spatial Sciences Institute at the University of Southern California Dornsife College of Letters, Arts and Sciences.

Her scientific research has focused on developing methods to improve the integration of environmental models with GIS from both the pedagogic and scientific perspectives and on formalizing the conceptual models of space acquired by scientists and humanities scholars across a wide range of disciplines.