An Approach to Automation of the Principle of Insignificance based on Fuzzy Descriptive Logic

With the advances in the applications of Artificial Intelligence to Law, tasks such as the representation of legal knowledge for organization/structuring, indexing/searching, and decision-making gain more space due to cost reduction, reduction of liabilities, and faster lawsuit applications. However, legal knowledge, such as laws and legal principles (such as the principle of meaninglessness or trifle), is generally subject to various semantic and syntactic anomalies with the presence of vagueness in its wording. One of the ways to automate processes, on the other hand, is using models and applications in descriptive logic, which has been conveniently used in Semantic Web applications because it is a decidable logic and with a higher level of expressiveness than classical propositional logic. On the other hand, Fuzzy Logic has been used since the last century in situations where knowledge needs to be understood in degrees of pertinence, given the present inaccuracies. This work, therefore, aims to propose an application based on fuzzy descriptive logic for the automation of the principle of insignificance. Furthermore, we also present the benefits and difficulties encountered when modeling applications in the legal domain through this logic.