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.
Highlights
Large volumes of geological reports have been accumulated during geological survey procedures, with each report containing different geological themes, such as rocks, minerals, or hydrology
This paper is based on previous research results, but it introduces technologies related to big data and semantics into the geological survey information cloud-computing platform (GSICCP)
Compared to traditional file-based storage, the use of the distributed architecture of the Hadoop system technology may have improved the security of the original data and improved the concurrent data access efficiency
Summary
Large volumes of geological reports have been accumulated during geological survey procedures, with each report containing different geological themes, such as rocks, minerals, or hydrology. This method proposes a domain knowledge base to model the latent semantic relationships among scientific data and services and an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining [32]. Our subsequent efforts concentrate on the following aspects of using geological big data: (1) Introduce geological thematic ontology, geological temporal ontology and toponymy ontology (these terms are short for the geological domain ontology), combine big-data storage and processing technologies, split unstructured geologic survey data into fragments, and extract multi-dimensional information from each fragment to rapidly discover target information from massive content.
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