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Automation of Legal Reasoning and Decision Based on Ontologies Mirna El Ghosh Automation of legal reasoning and decision based on ontologies Mirna El Ghosh To cite this version: Mirna El Ghosh. Automation of legal reasoning and decision based on ontologies. Web. Normandie Université, 2018. English. NNT : 2018NORMIR16. tel-02062174 HAL Id: tel-02062174 https://tel.archives-ouvertes.fr/tel-02062174 Submitted on 8 Mar 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE Pour obtenir le diplôme de doctorat Spécialité INFORMATIQUE Préparée au sein de « INSA de ROUEN » AUTOMATISATION DU RAISONNEMENT ET DECISION JURIDIQUES BASÉS SUR LES ONTOLOGIES Présentée et soutenue par Mirna El Ghosh Thèse soutenue publiquement le (24 septembre 2018) devant le jury composé de Prof / Chercheur / TTIG-CNR, the Legal Information Institute of M. Enrico FRANCESCONI Rapporteur the National Research Council of Italy Mme Zahia GUESSOUM MCF / HDR / Laboratoire d’Informatique de Paris 6 (LIP6) Rapporteur Mme Maroua BOUZID Prof / Professeur / Université de Caen-Basse Normandie, Examinateur Membre du GREYC UMR6072 Mme Cecilia ZANNI-MERK Prof / Professeur / INSA de ROUEN, LITIS Examinateur M. Habib ABDULRAB Prof / Professeur / INSA de ROUEN, LITIS Directeur de thèse M. Mohamad KHALIL Prof / Professeur / Université Libanaise, Faculté de Génie Codirecteur de thèse Mme Hala NAJA Prof / Professeur / Université Libanaise, Faculté des Sciences Co-encadrante de thèse Thèse dirigée par Habib ABDULRAB, INSA du Rouen, laboratoire LITIS et Mohamad KHALIL, Université Libanaise, Faculté de génie. To Julie Acknowledgements First, I gratefully acknowledge INSA de Rouen and LITIS laboratory for making it possible for me to carryout my research. My deepest gratitude goes to my thesis supervisor prof. Habib ABDULRAB for his invaluable advice, constant support and trust in me and without whom this work would never have been done. He provided me with many useful comments and suggestions for the preparation of this thesis. He also deserves my gratitude for providing me the opportunities to attend several conferences, workshops and meetings that have widen my research network. I extend my gratitude to my thesis co-supervisor prof. Hala NAJA for her insightful guidance, continuous effort and for her repeated reading of the work and offering comments that have led to many improvements over the years. I am grateful too for my thesis second co-supervisor prof. Mohamad KHALIL for his continuous support and encouragement during my research work. Besides my supervisors, I would like to thank the rest of my thesis committee: prof. Enrico FRANCESCONI, prof. Zahia GUESSOUM, prof. Maroua BOUZID and prof. Cecilia ZANNI-MERK, for their insightful comments and encouragement, but also for the hard question which incented me to widen my research from various perspectives. In particular, I am grateful for the reporters of this PhD for accepting the extra amount of work, for examining my PhD thesis and providing many helpful suggestions. Finally, i would like to thank my family: my husband, my parents, my sisters and brothers for supporting me spiritually throughout my thesis and my life. This work has been supported by the project CLASSE2 (Corridors Logistiques, Ap- plication à la Vallée de la Seine et à Son Environnement), CNRS Lebanon, Lebanese university and LaseR. Computer Science is no more about computers than astronomy is about telescopes. - EW Dijkstra, 1970 Abstract This thesis analyses the problem of building well-founded domain ontologies for reasoning and decision support purposes. Specifically, it discusses the building of legal ontologies for rule-based reasoning. In fact, building well-founded legal domain ontologies is considered as a difficult and complex process due to the complexity of the legal domain and the lack of methodologies. For this purpose, a novel middle-out approach called MIROCL is proposed. MIROCL tends to enhance the building process of well-founded domain ontologies by incorporating several support processes such as reuse, modulariza- tion, integration and learning. MIROCL is a novel modular middle-out approach for building well-founded domain ontologies. By applying the modularization process, a multi-layered modular architecture of the ontology is outlined. Thus, the intended ontology will be composed of four modules located at different abstraction levels. These modules are, from the most abstract to the most specific, UOM(Upper Ontology Module), COM(Core Ontology Module), DOM(Domain Ontology Module) and DSOM(Domain-Specific Ontology Module). The middle-out strategy is composed of two complementary strategies: top-down and bottom-up. The top-down tends to apply ODCM (Ontology-Driven Conceptual Modeling) and ontology reuse starting from the most abstract categories for build- ing UOM and COM. Meanwhile, the bottom-up starts from textual resources, by applying ontology learning process, in order to extract the most specific categories for building DOM and DSOM. After building the different modules, an integration process is performed for composing the whole ontology. The MIROCL approach is applied in the criminal domain for modeling legal norms. A well-founded legal domain ontology called CriMOnto (Criminal Modular Ontol- ogy) is obtained. Therefore, CriMOnto has been used for modeling the procedural aspect of the legal norms by the integration with a logic rule language (SWRL). Fi- nally, an hybrid approach is applied for building a rule-based system called CORBS. This system is grounded on CriMOnto and the set of formalized rules. 5 Contents List of Figures 10 List of Tables 15 1 Introduction 16 1.1 Overview................................... 17 1.2 Thesis Context................................ 17 1.2.1 Interest of AI in Law........................ 17 1.2.2 Artificial Intelligence and Law.................. 18 1.2.3 Legal Knowledge Based Systems (LKBS)............ 19 1.2.4 Role of Ontologies in the Automation of Legal Reasoning.. 20 1.2.5 The Semantic Web and Law.................... 21 1.3 Problem Statement............................. 22 1.3.1 Difficulty and Complexity of Well-founded Ontology Build- ing Process.............................. 22 1.3.2 Lack of Definition of Rule-based Legal Reasoning Models based on Domain Ontologies................... 23 1.4 Limitations of Existent Approaches.................... 23 1.5 Thesis Objectives.............................. 24 1.6 Thesis Contributions............................ 25 1.7 Structure of the Thesis........................... 27 2 Background 28 2.1 Overview................................... 28 2.2 Ontology Engineering........................... 29 2.2.1 Ontologies.............................. 29 2.2.1.1 From Philosophy to AI................. 29 2.2.1.2 Conceptualization and Ontologies........... 30 2.2.1.3 Definitions of Ontologies................ 33 2.2.1.4 Classifications of Ontologies.............. 34 2.2.1.5 Criteria of Ontologies.................. 36 2.2.1.6 Components of Ontologies............... 37 2.2.2 Foundational Ontologies..................... 39 2.2.2.1 The Unified Foundational Ontology UFO...... 39 6 Contents 2.2.3 Ontologies in the legal Domain.................. 46 2.2.3.1 The Functional Ontology of Law (FOLaw)...... 47 2.2.3.2 LRI-Core......................... 48 2.2.3.3 Ontology of Criminal Law (OCL.NL)......... 50 2.2.3.4 LKIF-Core........................ 50 2.2.3.5 DALOS Domain Ontology............... 53 2.2.3.6 Ontology of Professional Judicial Knowledge (OPJK) 56 2.2.3.7 UFO-L........................... 58 2.2.4 Roles and Uses of Legal Ontologies............... 59 2.2.5 Ontology Engineering Methodologies.............. 60 2.2.5.1 Uschold and colleagues................. 61 2.2.5.2 CommonKADS..................... 63 2.2.5.3 Methontology...................... 64 2.2.5.4 Ontology Development 101.............. 65 2.2.5.5 ON-TO-KNOWLEDGE Methodology (OTKM)... 66 2.2.5.6 SABiO: Systematic Approach for Building Ontologies 68 2.2.6 Ontology Engineering Tools and Environments........ 70 2.2.7 Ontology Languages and Formalisms.............. 73 2.2.7.1 RDF............................ 74 2.2.7.2 RDF Schema....................... 75 2.2.7.3 OWL............................ 75 2.2.7.4 OWL 2........................... 76 2.2.7.5 Description Logics (DL)................. 76 2.2.8 Ontology Engineering Support Processes............ 78 2.2.8.1 Ontology Learning................... 79 2.2.8.2 Ontology Reuse..................... 90 2.2.8.3 Ontology Modularization................ 93 2.2.8.4 Ontology Evaluation.................. 95 2.3 Knowledge Engineering.......................... 98 2.3.1 Modeling Principles in Knowledge Engineering........ 99 2.3.2 Knowledge Engineering Approaches.............. 101 2.3.2.1 CommonKADS..................... 101 2.3.2.2 MIKE........................... 103 2.3.2.3 PROTÉGÉ-II....................... 103 2.3.3 Legal Knowledge Engineering Approaches........... 104 2.3.3.1 Rule-Based Approach.................. 104 2.3.3.2 Model-Based Approach................. 105 2.4 Legal Rule-Based Systems......................... 108 2.4.1 Evaluation of Rule-Based Systems...............
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