This paper presents a refinement of PrOnto ontology using a validation test based on legal experts’ annotation of privacy policies combined with an Open Knowledge Extraction algorithm. Three iterations were performed, and a final test using new privacy policies. The results are 75% of detection of concepts and relationships in the policy texts and an increase of 29% in the accuracy using the new refined version of PrOnto enriched with SKOSXL lexicon terms and definitions.

PrOnto Ontology Refinement Through Open Knowledge Extraction

Palmirani Monica;Bincoletto Giorgia;Leone Valentina;Sapienza Salvatore;Sovrano Francesco
2019

Abstract

This paper presents a refinement of PrOnto ontology using a validation test based on legal experts’ annotation of privacy policies combined with an Open Knowledge Extraction algorithm. Three iterations were performed, and a final test using new privacy policies. The results are 75% of detection of concepts and relationships in the policy texts and an increase of 29% in the accuracy using the new refined version of PrOnto enriched with SKOSXL lexicon terms and definitions.
Legal Knowledge and Information Systems. JURIX 2019. The Thirty-second Annual Conference
205
210
FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
Palmirani Monica; Bincoletto Giorgia; Leone Valentina; Sapienza Salvatore; Sovrano Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/707825
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