Abstract:

【Purpose/significance】Starting from the semantic mining of multiple policy texts of open government data,the semantic rela⁃tionship among multiple policies is found and a method to reduce manual intervention and automate the analysis of multi-policy text synergies is explored【. Method/process】The data mining association rule algorithm is used to semantically mine the pre-processed pol⁃icy text of open government data,and the synergy between multiple policy texts is analyzed according to the obtained effective strong as⁃sociation【. Result/conclusion】Taking multiple policy texts of the open government data as the research object,the obtained number of effective strong association rules when the confidence is 0.7 and the lift is greater than 3 is relatively stable.After different levels of pol⁃icy text association rules analysis,it can get the conclusion basically consistent with the manual analysis.The conclusions prove that the method can be applied to the quantitative research of multi-policy text semantic synergy【. Innovation/limitation】The association rule algorithm is used to complete the knowledge reasoning research of multi-policy text semantic synergy and realize the problem of se⁃mantic automatic calculation.In the experiment the integrity of the policy vocabulary data preprocessing process parameter setting and other aspects will have an impact on the accuracy of the experimental results and the impact of manual intervention needs to be further reduced.