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A multi-granularity knowledge association model of geological text based on hypernetwork

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Abstract

With the explosive growth of geological data, considerable researches are focused on accurately retrieving specific information from massive data and fully exploiting the potential knowledge and information in unstructured data. Currently, the researches on unstructured content retrieval mostly ignore the association of semantics and knowledge or only consider the association of a singular granularity, which leads to the lost of concepts with the same semantics but expressed in different forms during the retrieval process. To address these problems, this paper has made some enhancements, and the main contributions include: (1) Define a decision rule, split unstructured geological survey data into content fragments, and construct a more fine-grained geologic textual semantic description model. (2) Present a multi-constraint fusion feature weighted model to extract the thematic feature items from the content fragments. (3) From the three granularity of document, content-item, feature-item, the associations of the same-granularity and cross-granularity are merged to construct a multi-granularity geological text hypernetwork model. (4) The experiments verify that the proposed approaches can improve the precision and recall rate of unstructured content retrieval.

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Acknowledgments

This work was funded by the National Natural Science Foundation of China (Grant No. 41671400) and the National Key Research and Development Program of China (Grant Nos. 2017YFB0503600, 2018YFB0505500, 2017YFC0602204, 2018YFB0505504). We thank the National Engineering Research Center of Geographic Information System for providing hardware support.

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Authors and Affiliations

  1. Faculty of Geography and Information Engineering, China University of Geosciences, Wuhan, China

    Can Zhuang, Wenjia Li, Zhong Xie & Liang Wu

  2. National Engineering Research Center for GIS, Wuhan, China

    Zhong Xie & Liang Wu

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  1. Can Zhuang
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Correspondence to Liang Wu.

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Communicated by: H. Babaie

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Zhuang, C., Li, W., Xie, Z. et al. A multi-granularity knowledge association model of geological text based on hypernetwork. Earth Sci Inform 14, 227–246 (2021). https://doi.org/10.1007/s12145-020-00534-w

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