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AI approaches to the complexity of legal systems : complex systems, the semantic web, ontologies, argumentation, and dialogue : international workshops AICOL-I/IVR-XXIV, Beijing, China, September 19…

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Cover of 'AI approaches to the complexity of legal systems : complex systems, the semantic web, ontologies, argumentation, and dialogue : international workshops AICOL-I/IVR-XXIV, Beijing, China, September 19, 2009, and, AICOL-II/JURIX 200, Rotterdam, the Netherlands, December 16, 2009 : revised selected papers'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction: Complex Systems and Six Challenges for the Development of Law and the Semantic Web
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    Chapter 2 As Law Goes By: Topology, Ontology, Evolution
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    Chapter 3 Sailing the Semantic Seas by Structural Vessels: Problems and Perspectives for the Identification of Implicit Knowledge in the Legal Domain
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    Chapter 4 Network Analysis of the French Environmental Code
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    Chapter 5 Model Regularity of Legal Language in Active Modifications
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    Chapter 6 Traceability and Change in Legal Requirements Engineering
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    Chapter 7 When a FrameNet-Style Knowledge Description Meets an Ontological Characterization of Fundamental Legal Concepts
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    Chapter 8 Application of an Ontology-Based Model to a Selected Fraudulent Disbursement Economic Crime
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    Chapter 9 Multi-layer Markup and Ontological Structures in Akoma Ntoso
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    Chapter 10 Prescriptive and Descriptive Obligations in Dynamic Epistemic Deontic Logic
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    Chapter 11 Lex Minus Dixit Quam Voluit , Lex Magis Dixit Quam Voluit : A Formal Study on Legal Compliance and Interpretation
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    Chapter 12 Legal Electronic Institutions and ONTOMEDIA: Dialogue, Inventio, and Relational Justice Scenarios
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    Chapter 13 Mediation, ODR, and the Web 2.0: A Case for Relational Justice
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    Chapter 14 Legal “Neutral Dialogue”, Implementing the Work of Bruce Ackerman in the Field of Law
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    Chapter 15 Legal Multimedia Management through JPEG2000 Framework
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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