Translating legislation into digital implementations is a labor-intensive process. It requires analyzing large volumes of legal text with a level of accuracy and reliability that typically demands a multidisciplinary team of both legal and IT experts. Currently, this work is largely done by hand, translating legislation into structured knowledge, creating legal knowledge graphs based on legal ontologies, where legal norms are translated into logical if-then rules. Although (generative) AI has been explored for interpreting legislation, it has so far not led to a major breakthrough. In practice, existing solutions have proven to be insufficiently reliable. As legal analysis comprises of many steps, our research investigates which steps of legal analysis are most likely to be accelerated or (partially) automated using AI, large language models and machine learning, without compromising accuracy and reliability. The initial focus is on identifying accurate candidate definitions for key legal concepts, including their precise knowledge references, given strict definition guidelines. Secondly, we build on our previous work (“A New Approach to Taxonomy Creation”) to generate candidate taxonomies with definitions written according to the same guidelines. In this tutorial we will share our results and give a live demo of what is currently possible. We look forward to active participation, discussing the most promising way forward and the challenges that arising when combining semantic and AI technology in the domain of legal analysis.
Bridging Law and AI: Semantic Precision without Compromising Trust