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Government policy-group integration and policy-chain inference are significant to the execution of strategies in current Chinese society. Specifically, the coordination of hierarchical policies implemented among government departments is one of the key challenges to rural revitalization. In recent years, various well-established quantitative methods have been proposed to evaluate policy coordination, but the majority of these relied on manual analysis, which can lead to subjective results. Thus, in this paper, a novel approach called "policy knowledge graph for the coordination among the government departments" (PG-CODE) is proposed, which incorporates topic modeling into policy knowledge graphs. Similar to a knowledge graph, a policy knowledge graph uses a graph-structured data model to integrate policy discourse. With latent Dirichlet allocation embedding, a policy knowledge graph could capture the underlying topics of the policies. Furthermore, coordination strength and topic diffusion among hierarchical departments could be inferred from the PG-CODE, as it can provide a better representation of coordination within the policy space. We implemented and evaluated the PG-CODE in the field of rural innovation and entrepreneurship policy, and the results effectively demonstrate improved coordination among departments.


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PG-CODE: Latent Dirichlet Allocation Embedded Policy Knowledge Graph for Government Department Coordination

Show Author's information Yilin KangRenwei OuYi ZhangHongling Li( )Shasha Tian
School of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
School of Public Management, South-Central University for Nationalities, Wuhan 430074, China

Abstract

Government policy-group integration and policy-chain inference are significant to the execution of strategies in current Chinese society. Specifically, the coordination of hierarchical policies implemented among government departments is one of the key challenges to rural revitalization. In recent years, various well-established quantitative methods have been proposed to evaluate policy coordination, but the majority of these relied on manual analysis, which can lead to subjective results. Thus, in this paper, a novel approach called "policy knowledge graph for the coordination among the government departments" (PG-CODE) is proposed, which incorporates topic modeling into policy knowledge graphs. Similar to a knowledge graph, a policy knowledge graph uses a graph-structured data model to integrate policy discourse. With latent Dirichlet allocation embedding, a policy knowledge graph could capture the underlying topics of the policies. Furthermore, coordination strength and topic diffusion among hierarchical departments could be inferred from the PG-CODE, as it can provide a better representation of coordination within the policy space. We implemented and evaluated the PG-CODE in the field of rural innovation and entrepreneurship policy, and the results effectively demonstrate improved coordination among departments.

Keywords: latent dirichlet allocation, policy knowledge graph, department coordination, topic diffusion, rural revitalization

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Received: 15 May 2021
Revised: 02 July 2021
Accepted: 30 July 2021
Published: 09 December 2021
Issue date: August 2022

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© The author(s) 2022

Acknowledgements

This work was supported by the National Social Science Fund of China (No. 20BGL231), and the Natural Science Foundation of Hubei Province (No. 2018CFB380).

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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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