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Scientific Programming
Volume 2017 (2017), Article ID 5072427, 13 pages
https://doi.org/10.1155/2017/5072427
Research Article

Intelligent Learning for Knowledge Graph towards Geological Data

Yueqin Zhu,1,2 Wenwen Zhou,2,3,4 Yang Xu,2,3,4 Ji Liu,2,3,4 and Yongjie Tan1,2

1Development and Research Center, China Geological Survey, Beijing 100037, China
2Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China
3School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
4Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

Correspondence should be addressed to Yueqin Zhu; moc.621@uhz_niqeuy and Yang Xu; nc.ude.btsu.sx@40306102b

Received 14 September 2016; Revised 21 December 2016; Accepted 12 January 2017; Published 16 February 2017

Academic Editor: HuaPing Liu

Copyright © 2017 Yueqin Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Knowledge graph (KG) as a popular semantic network has been widely used. It provides an effective way to describe semantic entities and their relationships by extending ontology in the entity level. This article focuses on the application of KG in the traditional geological field and proposes a novel method to construct KG. On the basis of natural language processing (NLP) and data mining (DM) algorithms, we analyze those key technologies for designing a KG towards geological data, including geological knowledge extraction and semantic association. Through this typical geological ontology extracting on a large number of geological documents and open linked data, the semantic interconnection is achieved, KG framework for geological data is designed, application system of KG towards geological data is constructed, and dynamic updating of the geological information is completed accordingly. Specifically, unsupervised intelligent learning method using linked open data is incorporated into the geological document preprocessing, which generates a geological domain vocabulary ultimately. Furthermore, some application cases in the KG system are provided to show the effectiveness and efficiency of our proposed intelligent learning approach for KG.