  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.
