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Automated Extraction of Regulated Information Types Using Hyponymy Relations

Requirements analysts can model regulated data practices to identify and reason about risks of non-compliance. If terminology is inconsistent or ambiguous, however, these models and their conclusions will be unreliable. To study this problem, we investigated an approach to automatically construct an information type ontology by identifying information type hyponymy in privacy policies using Tregex patterns. Tregex is a utility to match regular expressions against constituency parse trees, which are hierarchical expressions of natural language clauses, including noun and verb phrases. We discovered the Tregex patterns by applying content analysis to 15 privacy policies from three domains (shopping, telecommunication and social networks) to identify all instances of information type hyponymy. From this dataset, three semantic and four syntactic categories of hyponymy emerged based on category completeness and word-order. Among these, we identified and empirically evaluated 26 Tregex patterns to automate the extraction of hyponyms from privacy policies. The patterns identify information type hypernym-hyponym pairs with an average precision of 0.83 and recall of 0.52 across our dataset of 15 policies.