Identification and visual representation of explicit legal definitions, their relations and implicit actors in regulatory documents

The complexity and constantly rising volume of regulatory documents leads to tedious and error-prone manual analysis tasks. At the same time, Artificial Intelligence (AI) techniques offer new opportunities in handling legal information by, e.g., supporting legal stakeholder through automated knowledge acquisition. An example is the extraction of legal terms accompanied by their explanations in order to build a legal vocabulary or an ontology. A challenge aggravating this task is legal knowledge being implicitly stated. Thus, the occurrence of implicit actors, due to the usage of passive constructs in regulatory documents, is observed frequently. Consider the phrase “the provider keeps the data up to date” vs. “the data is kept up to date”. In the former phrase, the actor (provider) is explicit, while the latter requires additional context in order to determine who is keeping the data up to date. Hence, we provide an approach grounded in Natural Language Processing (NLP) to support the identification and clarification of explicit legal definitions and their relations. We then use this information to also identify implicit actors and make them explicit through insertion into the sentence. In addition, we provide a set of visual representations, including annotated documents, knowledge graphs, and statistics on how many legal definitions and implicit actors are present in an article. The evaluation is based on European regulations and demonstrates that explicit legal information can be used to clarify implicit information, enhancing the transparency and interpretability of complex legal documents.