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

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.