Legal documents, such as the FIA rulebook, are complex and difficult to navigate. Understanding these texts is time-consuming and prone to error. This thesis proposes using Natural Language Processing (NLP) and Knowledge Graphs (KGs) to transform legal texts into queryable, visual formats that simplify decision-making. Formula 1 will serve as a case study.
Objectives:
• Develop a pipeline to convert legal texts into navigable knowledge graphs.
• Create a queryable system for understanding relationships, exceptions, and dependencies.
• Detect inconsistencies and ambiguities in legal texts.
• Generalize the framework to apply to multiple legal domains. Approach
• Theoretical: Study legal text syntax/semantics, NLP techniques (e.g., BERT, GPT), and KG principles for modeling legal complexities.
• Practical:
o Build the KG using tools like Neo4j and visualize relationships between entities.
o Use ML algorithms to flag ambiguities or conflicts.
o Develop a natural language query interface for user-friendly interaction.