Fighting the Knowledge Representation Bottleneck with Large Language Models

This paper implements Large Language Models (LLMs) to support the development of expert systems in the legal domain. Our goal is to tackle one of the most critical issues related to the creation and management of rule-based systems, being the knowledge representation bottleneck. To do so, we employ GPT-4o in combination with an existing expert system developed using the Prolog language, presenting a case study based on multiple tasks. The first task deals with the formalization of legal articles in Prolog given a stable knowledge base and factual structure, including the revision of existing facts. The second task deals with the implementation of case law for updating of the expert system. To do so, it identifies the influence of case law on the application of existing norms, creates new rules and implements them in the system. This paper contributes to the field of law and Artificial Intelligence (AI) by investigating the relationship between LLMs and legal expert systems, and exploring its usefulness for knowledge engineers, as well as contributing to the research of hybrid architectures combining generative and symbolic AI.