Multilevel Legal Ontologies

Ajani, Gianmaria; Boella, Guido; Lesmo, Leonardo; Martín, Marco Antonio Gómez; Mazzei, Alessandro; Radicioni, Daniele Paolo; Rossi, Piercarlo

doi:10.1007/978-3-642-12837-0_8

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…Such capabilities can be useful in characterizing subtle and elusive meaning shift phenomena, such as diachronic sense modeling (Hu, Li, and Liang 2019) and conceptual misalignment, which is a well-known issue, for example, in the context of automatic translation. This issue has been approached, for the translation of European laws, through the design of formal ontologies (Ajani et al 2010).…”

Section: Discussionmentioning

confidence: 99%

LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items

Colla

Mensa

Radicioni

2020

Computational Linguistics

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We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the “blended” terminological vector along with those describing all senses associated to that term. LESSLEX has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity. We experimented over the principal data sets for such tasks in their multilingual and crosslingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LESSLEX vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.

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Section: Discussionmentioning

confidence: 99%

LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items

Colla

Mensa

Radicioni

2020

Computational Linguistics

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“…The ontology used in Eunomos is based on our Legal Taxonomy Syllabus [5,2]. The tool is based on a clear distinction between the notions of legal term and legal concept.…”

Section: Legal Ontologiesmentioning

confidence: 99%

“…Another feature of the Legal Taxonomy Syllabus developed in Ajani et al [2] is the ability to model the evolution of the meaning of concepts over time, depending on the amendment of the legislation defining them. When a new normative is approved and enacted it can define a number of new concepts; moreover it can happen that the same law can change a number of old concepts defined by old laws.…”

Section: Legal Ontologiesmentioning

confidence: 99%

Eunomos, a Legal Document and Knowledge Management System to Build Legal Services

Boella

Humphreys

Martín

et al. 2012

Lecture Notes in Computer Science

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This paper describes the Eunomos software, an advanced legal document and knowledge management system, based on legislative XML and ontologies. We describe the challenges of legal research in an increasingly complex, multi-level and multi-lingual world and how the Eunomos software helps users cut through the information overload to get the legal information they need in an organized and structured way and keep track of the state of the relevant law on any given topic. We describe the core system from workflow and technical perspectives, and discuss applications of the system for various user groups and our long term vision towards an Internet of Social Things, where laws can have an identity and be manipulated adding interpretation and can proactively inform interested users of their changes over time.

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“…Many legal ontologies have been proposed in the literature with different purposes and applied to different sub-domains, e.g., (Ajani et al, 2009;Hoekstra et al, 2007;Athan et al, 2015). However, their manual creation and maintenance is a very time-consuming and challenging task: domain-specific information needs to be created by legal experts to ensure the semantics of regulations is fully captured.…”

Section: Introductionmentioning

confidence: 99%

Legal NERC with ontologies, Wikipedia and curriculum learning

Cardellino¹,

Teruel²,

Alemany³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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In this paper, we present a Wikipediabased approach to develop resources for the legal domain. We establish a mapping between a legal domain ontology, LKIF (Hoekstra et al., 2007), and a Wikipediabased ontology, YAGO (Suchanek et al., 2007), and through that we populate LKIF. Moreover, we use the mentions of those entities in Wikipedia text to train a specific Named Entity Recognizer and Classifier. We find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights. However, this tool will be used as a preprocess for human annotation.We resort to a technique called curriculum learning aimed to overcome problems of overfitting by learning increasingly more complex concepts. However, we find that in this particular setting, the method works best by learning from most specific to most general concepts, not the other way round.

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Multilevel Legal Ontologies

Cited by 9 publications

References 18 publications

LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items

LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items

Eunomos, a Legal Document and Knowledge Management System to Build Legal Services

Legal NERC with ontologies, Wikipedia and curriculum learning

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