Law to Binary Tree -- An Formal Interpretation of Legal Natural Language
This work addresses the challenge of knowledge representation and reasoning in law for legal professionals and AI systems, though it appears incremental as it builds on existing legal science concepts.
The paper tackles the problem of automating legal analysis by proposing a new approach that interprets legal regulations as binary trees based on legal taxonomy, enabling legal reasoning systems to make decisions and resolve contradictions, with the result being a more understandable representation compared to existing sentence-based methods.
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.