A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

ISPRS International Journal of Geo-Information

Xia Lv¹  Chaoling Li¹  Mingqiang Guo²  Baode Jiang²  Zhanlong Chen³  Zhong Xie³  Liang Wu³  Lei Xue³ 

[1] Development and Research Center, China Geological Survey, Beijing 100037, China;National Engineering Research Center for GIS, Wuhan 430074, China;School of Information Engineering, China University of Geosciences, Wuhan 430074, China;

关键词: geology;    ontology;    knowledge discovery;    spatial data;    big data;   

DOI  :  10.3390/ijgi6060166

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown