<scp>GISphere</scp> Knowledge Graph for Geography Education: Recommending Graduate Geographic Information System/Science Programs

ABSTRACT The growing global interest in Geographic Information System/Science (GIS) programs has led to an increased demand for higher education in this field. However, students often struggle to identify suitable programs and faculty due to the overwhelming options and the lack of personalized guidance. This paper presents GISphere‐KG, an AI‐powered platform based on the GISphere project. It combines knowledge graph (KG) and large language models (LLMs) to enhance the search and recommendation of GIS‐related graduate programs. GISphere‐KG offers four key features: (1) interactive conversation that provides natural language responses to applicants' inquiries; (2) efficient information retrieval through semantic relationships built within the KG; (3) discovery of professors whose research interests align with those of the applicants, offering more choices within specific research fields; and (4) personalized program recommendations tailored to applicants' academic and career developments. Our platform aims to provide a user‐friendly tool that assists prospective students in achieving their career goals and enriching the geography community by attracting more talent and promoting global geography education.

Authors

Institutions

Publication Details

Journal
Transactions in GIS
Published
2024-12-03
DOI
https://doi.org/10.1111/tgis.13283
Citations
2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.69

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GISphere Knowledge Graph for Geography Education: Recommending Graduate Geographic Information System/Science Programs

Yanbing Chen, Yikang Wang, Kejin Wang, Yuhao Kang et al.
2 citations
Transactions in GIS
Topic Modeling
0.69
article

GISphere Knowledge Graph for Geography Education: Recommending Graduate Geographic Information System/Science Programs

Yanbing Chen, Yikang Wang, Kejin Wang, Yuhao Kang, Zhining Gu, Wenwen Li, Shan Ye, Bing Zhou, H. Gu
article en
2 citations

Abstract

ABSTRACT The growing global interest in Geographic Information System/Science (GIS) programs has led to an increased demand for higher education in this field. However, students often struggle to identify suitable programs and faculty due to the overwhelming options and the lack of personalized guidance. This paper presents GISphere‐KG, an AI‐powered platform based on the GISphere project. It combines knowledge graph (KG) and large language models (LLMs) to enhance the search and recommendation of GIS‐related graduate programs. GISphere‐KG offers four key features: (1) interactive conversation that provides natural language responses to applicants' inquiries; (2) efficient information retrieval through semantic relationships built within the KG; (3) discovery of professors whose research interests align with those of the applicants, offering more choices within specific research fields; and (4) personalized program recommendations tailored to applicants' academic and career developments. Our platform aims to provide a user‐friendly tool that assists prospective students in achieving their career goals and enriching the geography community by attracting more talent and promoting global geography education.

Transactions in GISVol. 29(1)
Louisiana State University (US), University of Wisconsin–Madison (US), University of South Carolina (US), Ghent University (BE), Emisphere Technologies (United States) (US), National Institute of Clean and Low-Carbon Energy (CN), University College London (GB), Arizona State University (US), The University of Texas at Austin (US), Texas A&M University (US)
Cartography and Geographic Information Society
Quality Education
Openalex Percentile: Top 23%
Topic Modeling
0.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.