Abstract
“There is a strong relationship/dependence between concepts and their linguistic terms, change on linguistic aspects may affect the intended meaning….” (Avicenna (980–1037 a.c.)) . The paper discusses the approaches to legal ontologies from a linguistic point of view. The starting point is that legal language depends upon the linguistic factor. Legal concepts are partly coming from the ordinary language and partly arising specifically from the legal domain. Both can be identified according to two different approaches:—bottom up and—top down. The attention will be focused on the analysis of the bottom-up approach which: implies two levels of analysis at lexical and ontological level; requires the integration of methodologies and tools in complex and modular architectures generally indicated as ontology learning from texts techniques. The discussion will be based on the assumption that the relationships among meanings are inferred by the analysis of the relationships of the linguistic expressions within texts and the assumption that some logic structures exist, specific for the legal domain, standing below the linguistic expressions of the law. The integration of a theoretical conceptual model with the lexical level extracted from texts allows to respect the contextuality of the law. Therefore, the need to bridge the gap between lexicons and the ontological layer will be underlined focusing on methodologies that can be put in place for integrating these two levels.
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Notes
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A reformulation of Ross theory on legal concepts in terms of ontological analysis is in Sartor (2007).
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http://www.estrellaproject.org/lkif-core (Breuker et al.2007)
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wordnet.princeton.edu and http://www.globalwordnet.org
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Lois, Lexical ontologies for legal information sharing(EDC 2026–…)
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“A lexicon is not a very good ontology. An ontology, after all, is a set of categories of objects or ideas in the world, along with certain relationships among them; it is not a linguistic object. A lexicon, on the other hand, depends, by definition, on a natural language and the word senses in it..[…] Despite all the discussion in the previous section, it is possible that a lexicon with a semantic hierarchy might serve as the basis for a useful ontology, and an ontology may serve as a grounding for a lexicon. This may be so in particular in technical domains, in which vocabulary and ontology are more closely tied than in more-general domains” (Hirst 2004).
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The Owl meta.ontology for conneting Wordnets is composed by three classes: Synset, WordSense and Word. Each Synset object is a set of WordSense objects since polysemous terms are distinct in wordsenses http://www.w3.org/TR/wordnet-rdf/
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The tools, specifically addressed to process English and other EU language texts, are GATE and T2K. GATE supports advanced language analysis owned/provided and maintained by the Department of Computer Science of the University of Sheffield. T2K is a terminology extractor and ontology learning tool for the Italian language jointly developed by CNR-ILC and the University of Pisa.
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In order to guarantee the linking of acquired domain terms to the individual textual partitions rather than to the individual act, the corpus to be processed was segmented into 8,192 files corresponding to 2,583 directive partitions (sub-paragraphs) and 5,609 case law partitions.
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The selected minimum frequency threshold for both single and multiword terms was 5, the percentage of selected terms from the ranked lists was 20% in the case of single terms (both single and multiword terms), terms and 70% for multiword terms. The Italian TermBank is composed of 1,443 of which 1,168 are multiword terms of different complexity. The number of extracted hyponymic relations is 623 referring to 229 hypernym terms, whereas the number of identified related terms is 1,258 referring to 279 terminological headwords. The processing of the English corpus resulted in a set of 3,012 terms, which consists of 1,157 multi word units and 1,855 single word terms. This set has an overlap of 572 terms with the LOIS vocabulary. Crosslingual alignment computes the overlap between the different languages according to two criteria: (a) the positional similarity in the texts; (b) (near)equivalents on the basis of translations (through WordNet) or, if they share some elements, that are translations. More details are in Agnoloni et al. (2009).
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Biasiotti, M.A., Tiscornia, D. (2011). Legal Ontologies: The Linguistic Perspective. 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_9
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