Abstract
In this paper we first briefly summarize the process used for building ontology from a legal corpus given in natural language. Current ontology-building supposes a particular structure and a finite number of relation types. The corresponding architecture is mainly driven by tree-like structures that capture a part of the full complexity that is effectively at work in any legal system. We propose to endow a legal ontology with further functionalities related to its mapping in a given corpus. We define posterior probability functions related to the frequency of occurrence of any term or concept, and information functions that measure the mutual information shared by terms in the corpus, whatever might be the a priori links represented between them in the ontology. We then show how these probabilistic tools can be also associated with a scale-dependent view on the network structure of a legal corpus (from the larger scale of the network of all codes or laws of a legal system, to the much finer scale of articles). New perspectives mixing semantic web and some properties of complex systems are described.
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Notes
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ARCHtecture for ONTOlogical Elaborating
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Note that if C is a large legal corpus, the zero occurrence of a concept x somewhat disqualifies x as a component of the legal ontology (though x is likely to belong to a core ontology).
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We find the intuitive notion of a measure that was elaborated by E. Borel in the development of a mathematical theory of measure funding the probability theory (see Halmos 1974).
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For example we might be interested in analyzing the legal relationships between the various administrative courts or between different legal bodies in charge of the management of the Intellectual Property Rights in the French or European legal systems. These relationships are themselves relevant legal concepts.
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I AMI [x, x]C the self-information function is not trivially 1 and can be mapped as well on a corpus.
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Considering these examples, the jurisprudence would be a very interesting corpus to explore with such information mapping tools, with also the possibility to consider particular periods of special social interest or technological developments.
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In this work we only consider the Legislative Part of the CIP.
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It is interesting to note that this higher mutual information level is still resulting from a smaller number of occurrences of the joint event [reproduction, placing at the disposal of ] with regard to the joint event [representation , reproduction]. The reason is that the systematic joint occurrence of rare terms is highly mutually informative.
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This is a general property of most codes. For example there are 1,266 articles in the 2007 version of the legislative part of the Environmental Code (Bourcier and Mazzega 2007b).
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Acknowledgments
R. Boulet benefits from a post doctoral grant of the Institut National des Sciences de l’Univers (CNRS, Paris). This study is partly funded by the Réseau de Thématique de Recherche Avancées « Sciences et Techniques de l’Aéronautique et de l’Espace » (RTRA STAE) in Toulouse, France, under the MAELIA project (http://www.iaai-maelia.eu/). The yEd Graph editor has been used for producing the Figs. 7.3 and 7.4.
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Mazzega, P., Bourcier, D., Bourgine, P., Nadah, N., Boulet, R. (2011). A Complex-System Approach: Legal Knowledge, Ontology, Information and Networks. In: Sartor, G., Casanovas, P., Biasiotti, M., Fernández-Barrera, M. (eds) Approaches to Legal Ontologies. Law, Governance and Technology Series, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0120-5_7
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