A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph
This addresses compliance automation for regulators and businesses, but it is incremental as it applies existing methods to a new domain-specific dataset.
The paper tackled automating compliance by constructing an executable Knowledge Graph from Abu Dhabi Global Market regulations using BERT-based models, achieving automated tagging and querying with Cypher, and open-sourcing the graph on GitHub for future use.
The paper presents a study on using language models to automate the construction of executable Knowledge Graph (KG) for compliance. The paper focuses on Abu Dhabi Global Market regulations and taxonomy, involves manual tagging a portion of the regulations, training BERT-based models, which are then applied to the rest of the corpus. Coreference resolution and syntax analysis were used to parse the relationships between the tagged entities and to form KG stored in a Neo4j database. The paper states that the use of machine learning models released by regulators to automate the interpretation of rules is a vital step towards compliance automation, demonstrates the concept querying with Cypher, and states that the produced sub-graphs combined with Graph Neural Networks (GNN) will achieve expandability in judgment automation systems. The graph is open sourced on GitHub to provide structured data for future advancements in the field.