Law to Binary Tree -- An Formal Interpretation of Legal Natural Language

TL;DR

This paper introduces a novel method for representing legal regulations as binary trees, enhancing interpretability and reasoning capabilities in legal AI systems.

Contribution

It proposes a formal interpretation of legal language using binary trees, improving legal reasoning and understanding over traditional sentence-based methods.

Findings

Binary tree representation facilitates legal reasoning.

The approach improves interpretability of legal regulations.

Example demonstrates practical application of the method.

Abstract

Knowledge representation and reasoning in law are essential to facilitate the automation of legal analysis and decision-making tasks. In this paper, we propose a new approach based on legal science, specifically legal taxonomy, for representing and reasoning with legal documents. Our approach interprets the regulations in legal documents as binary trees, which facilitates legal reasoning systems to make decisions and resolve logical contradictions. The advantages of this approach are twofold. First, legal reasoning can be performed on the basis of the binary tree representation of the regulations. Second, the binary tree representation of the regulations is more understandable than the existing sentence-based representations. We provide an example of how our approach can be used to interpret the regulations in a legal document.