Paper Info
Title
A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data.
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.
Year
DOI
Venue
2017
10.3390/ijgi6060166
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
Field
DocType
geology,ontology,knowledge discovery,spatial data,big data
Data science,Ontology (information science),Information retrieval,Unstructured data,NoSQL,Knowledge extraction,Geological survey,Geography,Workflow,Big data,Information discovery
Journal
Volume
Issue
Citations 
6
6
2
PageRank 
References 
Authors
0.37
40
8
Name
Order
Citations
PageRank
Liang Wu1335.49
Lei Xue210316.03
Chaoling Li320.37
Xia Lv420.37
Zhanlong Chen5315.82
Baode Jiang620.37
Mingqiang Guo793.90
Zhong Xie84813.30