LEGAL KNOWLEDGE REPRESENTATION MODEL

Abstract

Background. The need to automate the decision-making process on legal issues in various fields of human activity determines the importance of this work. The purpose of the article is to demonstrate a new approach to organizing and structuring legal knowledge using semantic networks. Materials and methods. The basic methods of knowledge representation are considered. Graph theory as a mathematical apparatus, as well as Text Mining methods for extracting information from text documents were used. Results. The advantages of semantic networks as a knowledge representation model are shown. An approach to the automated formation of a knowledge base based on legal text documents is presented. An example of its practical application is given. Conclusions. The described model opens up broad prospects for the automation of legal information processing. The considered approach can be used both for solving frequently encountered practical decision-making legal issues, and for the timeconsuming task of automating the regulatory legal acts examination.

Keywords

• knowledge representation model
• semantic network
• structuring
• automating
• legal knowledge