Abstract
Reading, analyzing, and implementing regulatory documents are cumbersome and still mostly manual tasks. The constantly increasing amount of regulatory documents causes an equally increasing need for supporting those tasks through information systems. One key aspect for accomplishing those tasks is to acquire knowledge of the different legal definitions, i.e., legal terms accompanied by their explanations, in order to build a legal vocabulary or an ontology. This paper proposes an approach taking European regulations as input and i) automatically extracting legal definitions, ii) determining semantic relations such as hyponyms, meronyms, and synonyms between legal terms, and iii) visualizing the results in form of a knowledge graph and statistics. The approach is evaluated on European regulations and made accessible particularly for non-technical users through an easy-to-use web service.
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References
Al-Talib, G.A., Atiyah, A.A.: Extraction and classification of semantic relations from news recommendation. In: International Congress on Human-Computer Interaction, Optimization and Robotic Applications (2022). https://doi.org/10.1109/HORA55278.2022.9799885
Amaludin, B., Wardika, F.R., Putra, P.J.M., Paramartha, I.G.Y.: Analyze the usage of legal definitions in Indonesian regulation using text mining case study: treasury and budget law. In: Legal Knowledge and Information Systems (2021). https://doi.org/10.3233/FAIA210324
Avram, A., Cercel, D., Chiru, C.: UPB at SemEval-2020 task 6: pretrained language models for definition extraction. In: Workshop on Semantic Evaluation (2020). https://doi.org/10.18653/v1/2020.semeval-1.97
Commission, E., service, L.: Joint practical guide of the European Parliament, the Council and the Commission for persons involved in the drafting of European Union legislation. Publications Office of the European Union (2016). https://doi.org/10.2880/5575
Cruse, D.A.: Hyponymy and its varieties. Semant. Relationsh. Interdiscip. Perspect. (2002). https://doi.org/10.1007/978-94-017-0073-3_1
Culy, C., Chiocchetti, E., Ralli, N.: Visualizing conceptual relations in legal terminology. In: International Conference on Information Visualisation (2013). https://doi.org/10.1109/IV.2013.42
Damaratskaya, A.: Identification and Visualization of Legal Definitions and their Relations Based on European Regulatory Documents. Bachelor’s thesis, Technical University Munich, May 2023. https://mediatum.ub.tum.de/doc/1715461/document.pdf
European Parl., Council of the EU: Regulation (EU) 2016/679 of the European Parliament and of the Council. https://data.europa.eu/eli/reg/2016/679/oj
European Parl., Council of the EU: Regulation (EU) 2019/1241 of the European Parliament and of the Council. https://eur-lex.europa.eu/eli/reg/2019/1241/oj
European Parl., Council of the EU: Regulation (EU) 2020/740 of the European Parliament and of the Council. http://data.europa.eu/eli/reg/2020/740/oj
European Parl., Council of the EU: Regulation (EU) 2021/947 of the European Parliament and of the Council. http://data.europa.eu/eli/reg/2021/947/oj
Ferneda, E., do Prado, H.A., Batista, A.H., Pinheiro, M.S.: Extracting definitions from Brazilian legal texts. In: Murgante, B., et al. (eds.) ICCSA 2012. LNCS, vol. 7335, pp. 631–646. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31137-6_48
Ferrara, A., Picascia, S., Riva, D.: Context-aware knowledge extraction from legal documents through zero-shot classification. In: Guizzardi, R., Neumayr, B. (eds.) Advances in Conceptual Modeling. ER 2022. LNCS, vol. 13650, pp. 81–90. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-22036-4_8
Hippisley, A., Cheng, D., Ahmad, K.: The head-modifier principle and multilingual term extraction. Nat. Lang. Eng. 11(2), 129–157 (2005). https://doi.org/10.1017/S1351324904003535
Hwang, R.H., Hsueh, Y.L., Chang, Y.T.: Building a Taiwan law ontology based on automatic legal definition extraction. Appl. Syst. Innov. (2018). https://doi.org/10.3390/asi1030022
Khoo, C.S., Na, J.C.: Semantic relations in information science. Annu. Rev. Inf. Sci. Technol. (2006). https://doi.org/10.1002/aris.1440400112
de Maat, E., Winkels, R.: Automated classification of norms in sources of law. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS (LNAI), vol. 6036, pp. 170–191. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12837-0_10
Nakamura, M., Ogawa, Y., Toyama, K.: Extraction of legal definitions and their explanations with accessible citations. In: Casanovas, P., Pagallo, U., Palmirani, M., Sartor, G. (eds.) AICOL -2013. LNCS (LNAI), vol. 8929, pp. 157–171. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45960-7_12
Nakamura, M., Ogawa, Y., Toyama, K.: Extraction of legal definitions from a Japanese statutory corpus-toward construction of a legal term ontology. In: Law via the Internet Conference (2013)
Sartor, G., Santin, P., Audrito, D., Sulis, E., Caro, L.D.: Automated extraction and representation of citation network: a CJEU case-study. In: Guizzardi, R., Neumayr, B. (eds.) Advances in Conceptual Modeling. ER 2022. LNCS, vol. 13650, pp. 102–111. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-22036-4_10
Singh, A., Kumar, P., Sinha, A.: DSC IIT-ISM at semeval-2020 task 6: boosting BERT with dependencies for definition extraction. In: Workshop on Semantic Evaluation (2020). https://doi.org/10.18653/v1/2020.semeval-1.93
Waltl, B., et al.: Automated extraction of semantic information from German legal documents (2017)
Winkels, R., Hoekstra, R.: Automatic extraction of legal concepts and definitions. In: Legal Knowledge and Information Systems (2012). https://doi.org/10.3233/978-1-61499-167-0-157
Winter, K., Gall, M., Rinderle-Ma, S.: Regminer: taming the complexity of regulatory documents for digitalized compliance management. In: Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Track at BPM. CEUR Workshop Proceedings, vol. 2673, pp. 112–116. CEUR-WS.org (2020). https://ceur-ws.org/Vol-2673/paperDR10.pdf
Winter, K., Rinderle-Ma, S.: Untangling the GDPR using conrelminer. CoRR (2018). http://arxiv.org/abs/1811.03399
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Sai, C., Damaratskaya, A., Winter, K., Rinderle-Ma, S. (2023). Identification and Visualization of Legal Definitions and Legal Term Relations. In: Sales, T.P., Araújo, J., Borbinha, J., Guizzardi, G. (eds) Advances in Conceptual Modeling. ER 2023. Lecture Notes in Computer Science, vol 14319. Springer, Cham. https://doi.org/10.1007/978-3-031-47112-4_14
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