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Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation

by 1,2,3, 1,2,3,*, 1,2,3, 1,2,3, 1,2,3 and 1,2,3
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 184;
Received: 25 February 2019 / Revised: 15 March 2019 / Accepted: 4 April 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation. View Full-Text
Keywords: geographic knowledge representation; geographic knowledge graph; formalization; GeoKG geographic knowledge representation; geographic knowledge graph; formalization; GeoKG
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MDPI and ACS Style

Wang, S.; Zhang, X.; Ye, P.; Du, M.; Lu, Y.; Xue, H. Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation. ISPRS Int. J. Geo-Inf. 2019, 8, 184.

AMA Style

Wang S, Zhang X, Ye P, Du M, Lu Y, Xue H. Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation. ISPRS International Journal of Geo-Information. 2019; 8(4):184.

Chicago/Turabian Style

Wang, Shu, Xueying Zhang, Peng Ye, Mi Du, Yanxu Lu, and Haonan Xue. 2019. "Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation" ISPRS International Journal of Geo-Information 8, no. 4: 184.

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