Constructing Public Policy Risk Early Warning and Response Mechanisms through Integrated Knowledge Graphs

With the development of today's public governance increasingly complex, the need for an effective policy risk early warning and response mechanism to help the government departments and social organizations to make accurate decisions. Combining existing knowledge graph construction and semantic reasoning framework, this paper proposes to add multimodal data fusion, advanced event detection and policy risk quantitative analysis module to form a multi-level and dynamic change public policy risk early warning response model. Secondly, the evitable policy-related emergencies and the changes of public opinion are monitored by the multi-source heterogeneous data fusion and the event detection mechanism, and the knowledge graph is updated in real time. Finally, through the reinforcement learning-based risk quantitative analysis and scenario simulation module to carry out the prediction, evaluation and response plan generation of potential policy risks, a public policy risk early warning response of the whole link closed loop is constructed. Experimental results demonstrate that the proposed model is better than the current methods in risk identification accuracy, response timeliness and adaptability to complex policy scenarios.