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A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data


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1

School of Information Engineering, China University of Geosciences, Wuhan 430074, China

2

National Engineering Research Center for GIS, Wuhan 430074, China

3

Development and Research Center, China Geological Survey, Beijing 100037, China





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Author to whom correspondence should be addressed.



Academic Editors: Ozgun Akcay and Wolfgang Kainz

Abstract Geologic survey procedures accumulate large volumes of structured and unstructured data. Fully exploiting the knowledge and information that are included in geological big data and improving the accessibility of large volumes of data are important endeavors. In this paper, which is based on the architecture of the geological survey information cloud-computing platform GSICCP and big-data-related technologies, we split geologic unstructured data into fragments and extract multi-dimensional features via geological domain ontology. These fragments are reorganized into a NoSQL Not Only SQL database, and then associations between the fragments are added. A specific class of geological questions was analyzed and transformed into workflow tasks according to the predefined rules and associations between fragments to identify spatial information and unstructured content. We establish a knowledge-driven geologic survey information smart-service platform GSISSP based on previous work, and we detail a study case for our research. The study case shows that all the content that has known relationships or semantic associations can be mined with the assistance of multiple ontologies, thereby improving the accuracy and comprehensiveness of geological information discovery. View Full-Text

Keywords: geology; ontology; knowledge discovery; spatial data; big data geology; ontology; knowledge discovery; spatial data; big data





Autor: Liang Wu 1,2, Lei Xue 1, Chaoling Li 3, Xia Lv 3, Zhanlong Chen 1, Baode Jiang 2, Mingqiang Guo 2 and Zhong Xie 1,2,*

Fuente: http://mdpi.com/



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