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Open Access

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

School of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
School of Public Management, South-Central University for Nationalities, Wuhan 430074, China
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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.

References

[1]
Q. X. Han, The thought of people’s co-creation and sharing: Systematic analysis of the new thought of the CPC central committee on state governance, (in Chinese), Journal of the Party School of the Central Committee of the C. P. C., vol. 20, no. 1, pp. 15-27, 2016.
[2]
A. G. Li and Q. Zeng, Policy situation and the next prospect of widespread entrepreneurship and innovation, (in Chinese), Reform, no. 10, pp. 149-157, 2017.
[3]
H. Y. Zeng, Implementation progress and suggestions of entrepreneurship and innovation, (in Chinese), Macroeconomic Management, no. 12, pp. 21-23, 2015.
[4]
Z. S. Zhang and X. T. Li, Performance appraisal and research on policy of innovation and entrepreneurship based on the DEA model—Take Tianjin business incubators as the analysis objects, (in Chinese), Journal of Tianjin University (Social Sciences), vol. 18, no. 5, pp. 385-391, 2016.
[5]
C. E. Hughes, A. Ritter, and N. Mabbitt, Drug policy coordination: Identifying and assessing dimensions of coordination, Int. J. Drug Policy, vol. 24, no. 3, pp. 244- 250, 2013.
[6]
J. S. Peng, W. G. Zhong, and W. X. Sun, Policy measurement, policy coordinated evolution and economic performance: An empirical study based on innovation policy, (in Chinese), Management World, no. 9, pp. 25-36, 2008.
[7]
S. Stathopoulou, D. Psaltopoulos, and D. Skuras, Rural entrepreneurship in Europe, Int. J. Entrep. Behav. Res., vol. 10, no. 6, pp. 404-425, 2004.
[8]
S. J. Goetz, M. Partridge, S. C. Deller, and D. A. Fleming, Evaluating U.S. rural entrepreneurship policy, J. Reg. Anal. Policy, vol. 40, no. 1, pp. 20-33, 2010.
[9]
J. Murdoch, Networks—A new paradigm of rural development? J. Rural Stud., vol. 16, no. 4, pp. 407-419, 2000.
[10]
G. D. Libecap, Economic variables and the development of the law: The case of western mineral rights, The Journal of Economic History, vol. 38, no. 2, pp. 338-362, 1978.
[11]
X. Y. Liu, Y. R. Pang, W. S. Hou, and X. H. Shan, Research on the coordination of S&T innovation policies between central and local governments from the perspective of relation-content, (in Chinese), Forum on Science and Technology in China, no. 12, pp. 13-21, 2020.
[12]
D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
[13]
H. Jelodar, Y. L. Wang, C. Yuan, X. Feng, X. H. Jiang, Y. C. Li, and L. Zhao, Latent dirichlet allocation (LDA) and topic modeling: Models, applications, a survey, Multimed. Tools Appl., vol. 78, no. 11, pp. 15169-15211, 2019.
[14]
B. Chen, L. L. Zhu, D. Kifer, and D. Lee, What is an opinion about? Exploring political standpoints using opinion scoring model, in Proc. 24th AAAI Conf. Artificial Intelligence, Atlanta, GA, USA, 2010, pp. 1007-1012.
[15]
R. Cohen and D. Ruths, Classifying political orientation on twitter: It’s not easy! in Proc. 7th Int. AAAI Conf. Weblogs and Social Media, Cambridge, MA, USA, 2013.
[16]
D. Greene and J. P. Cross, Unveiling the political agenda of the European parliament plenary: A topical analysis, in Proc. ACM Web Science Conf., Oxford, UK, 2015, p. 2.
[17]
Y. Shirota, Y. Yano, T. Hashimoto, and T. Sakura, Monetary policy topic extraction by using LDA: Japanese monetary policy of the second ABE cabinet term, presented at 2015 IIAI 4th Int. Congress on Advanced Applied Informatics, Okayama, Japan, 2015, pp. 8-13.
[18]
S. S. Jia and B. G. Wu, Incorporating LDA based text mining method to explore new energy vehicles in China, IEEE Access, vol. 6, pp. 64596-64602, 2018.
[19]
J. Zhao, H. F. Li, and C. G. Li, Analysis of research topic evolution of coordinated development of Beijing-Tianjin-Hebei based on probabilistic topic models, (in Chinese), Sci. Technol. Eng., vol. 19, no. 36, pp. 225-234, 2019.
[20]
P. T. Xie, D. Y. Yang, and E. Xing, Incorporating word correlation knowledge into topic modeling, in Proc. 2015 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, CO, USA, 2015, pp. 725-734.
[21]
L. Yao, Y. Zhang, B. G. Wei, H. Z. Qian, and Y. B. Wang, Incorporating probabilistic knowledge into topic models, in Proc. 19th Pacific-Asia Conf. Knowledge Discovery and Data Mining, Ho Chi Minh City, Vietnam, 2015, pp. 586-597.
[22]
R. B. Xie, Z. Y. Liu, J. Jia, H. B. Luan, and M. S. Sun, Representation learning of knowledge graphs with entity descriptions, in Proc. 30th AAAI Conf. Artificial Intelligence, Phoenix, AZ, USA, 2016.
[23]
L. Yao, Y. Zhang, B. G. Wei, Z. Jin, R. Zhang, Y. Y. Zhang, and Q. F. Chen, Incorporating knowledge graph embeddings into topic modeling, in Proc. 31st AAAI Conf. Artificial Intelligence, San Francisco, CA, USA, 2017, pp. 3119-3126.
[24]
J. B. Qu and S. Y. Ou, Analyzing topic evolution with topic filtering and relevance, (in Chinese), Data Analysis and Knowledge Discovery, vol. 2, no. 1, pp. 64-75, 2018.
Tsinghua Science and Technology
Pages 680-691
Cite this article:
Kang Y, Ou R, Zhang Y, et al. PG-CODE: Latent Dirichlet Allocation Embedded Policy Knowledge Graph for Government Department Coordination. Tsinghua Science and Technology, 2022, 27(4): 680-691. https://doi.org/10.26599/TST.2021.9010059
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Received: 15 May 2021
Revised: 02 July 2021
Accepted: 30 July 2021
Published: 09 December 2021
© The author(s) 2022

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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