Populating Legal Ontologies using Information Extraction based on Semantic Role Labeling and Text Similarity

This thesis seeks to address the problem of the 'resource consumption bottleneck' of creating (legal) semantic technologies manually. It builds on research in legal theory, ontologies and natural language processing in order to semi-automatically normalise legislative text, extract definitions and structured norms, and link normative provisions to recitals. The output is intended to help make laws more accessible, understandable, and searchable in a legal document management system. Key contributions are:
- an analysis of legislation and structured norms in legal ontologies and compliance systems in order to determine the kind of information that individuals and organisations require from legislation to understand their rights and duties;
- an analysis of the semantic and structural challenges of legislative text for machine understanding;
- a rule-based normalisation module to transform legislative text into regular sentences to facilitate natural language processing;
- a Semantic Role Labeling based information extraction module to extract definitions and norms from legislation and represent them as structured norms in legal ontologies;
- an analysis of the impact of recitals on the interpretation of legislative norms;
- a Cosine Similarity based text similarity module to link recitals to relevant normative provisions;
- a description of important challenges that have emerged from this research which may prove useful for future work in the extraction and linking of information from legislative text.