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Open Access

Natural Disasters Warning for Enterprises Through Fuzzy Keywords Search

Politics and Public Administration College, Qufu Normal University, Rizhao 276826, China.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.
Foreign Languages College, Weifang University, Weifang 261000, China.
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Abstract

With the ever-increasing number of natural disasters warning documents in document databases, the document database is becoming an economic and efficient way for enterprise staffs to learn and understand the contents of the natural disasters warning through searching for necessary text documents. Generally, the document database can recommend a mass of documents to the enterprise staffs through analyzing the enterprise staff’s precisely typed keywords. In fact, these recommended documents place a heavy burden on the enterprise staffs to learn and select as the enterprise staffs have little background knowledge about the contents of the natural disasters warning. Thus, the enterprise staffs fail to retrieve and select appropriate documents to achieve their desired goals. Considering the above drawbacks, in this paper, we propose a fuzzy keywords-driven Natural Disasters Warning Documents retrieval approach (named NDWDkeyword). Through the text description mining of documents and the fuzzy keywords searching technology, the retrieval approach can precisely capture the enterprise staffs’ target requirements and then return necessary documents to the enterprise staffs. Finally, a case study is run to explain our retrieval approach step by step and demonstrate the effectiveness and feasibility of our proposal.

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Tsinghua Science and Technology
Pages 558-564
Cite this article:
Sun Z, Liu H, Yan C, et al. Natural Disasters Warning for Enterprises Through Fuzzy Keywords Search. Tsinghua Science and Technology, 2021, 26(4): 558-564. https://doi.org/10.26599/TST.2020.9010027

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Received: 10 July 2020
Revised: 02 August 2020
Accepted: 11 August 2020
Published: 04 January 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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