Path Updating of the Legal Ontology Model for Personal Information Protection in the Age of Big Data

Abstract

Abstract This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.