This paper is concerned with the goal of maintaining legal information and compliance systems: the ‘resource consumption bottleneck’ of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in legal document management systems such as Eunomos (Boella et al., 2016).

In this paper, we describe how NLP can semi-automate the construction and analysis of knowledge in Eunomos, a legal knowledge management service which enables users to view legislation from various sources and find the right definitions and explanations of legal concepts in a given context. NLP can semi-automate some routine tasks currently performed by knowledge engineers, such as classifying norm, or linking key terms within legislation to ontological concepts. This helps overcome the resource bottleneck problem of creating specialist knowledge management systems. While accuracy is of the utmost importance in the legal domain, and the information should be verified by domain experts as a matter of course, a semi-automated approach can result in considerable efficiency gains.