MISLA²: A System to Information Retrieval in Labour Lawsuits using Legal Ontologies and Regular Expressions
Electronic Legal Proceedings are a worldwide legal phenomena, allowing the use of computerized systems for the creation and monitoring of procedural acts in the most diverse legal bodies. On one hand, it allows greater transparency in the conduct of procedural acts, on the other, it has contributed to the bottleneck of open but unresolved lawsuits each year. Nowadays, Information Retrieval to automate the processing of these procedural objects is at the forefront of computer systems for Law. In this study, we present MISLA2, a system to retrieve orders and preliminaries from judicial labour sentences through ontological models built from previous cases. Instead of tied and difficult-to-maintain domain specification models, we demonstrate how light ontologies, in conjunction with regular expressions for extracting significant portions of the text, can achieve the desired results. In addition, empirical experiments carried out with real labour lawsuits evidence that results are quite promising.
Asim, M. N., Wasim, M., Ghani Khan, M. U., Mahmood, N., and Mahmood, W. (2019). The use of ontology in retrieval: A study on textual, multilingual, and multimedia retrieval. IEEE Access, 7:21662–21686.
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D., and Patel-Schneider, P. F., editors (2010). The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York, NY, USA, 2 edition.
Berk, R. and Hyatt, J. (2015). Machine learning forecasts of risk to inform sentencing decisions. Federal Sentencing Reporter, 27(4):222–228.
Brasil (2017). Lei nº 13.467, de 13 de julho de 2017. Diário Oficial [da] República Federativa do Brasil.
Casellas, N. (2011). Legal Ontology Engineering: Methodologies, Modelling Trends, and the Ontology of Professional Judicial Knowledge. Law, Governance and Technology. Springer Netherlands, Barcelona, Spain.
Corcho, O., Fernandez-Lopez, M., and Gómez-Pérez, A. (2006). Ontological engineering: Principles, methods, tools and languages. In Calero, C., Ruiz, F., and Piattini, M., editors, Ontologies for Software Engineering and Software Technology, pages 1–48. Springer Berlin Heidelberg.
Corcho, O., Fernández-López, M., Gómez-Pérez, A., and López-Cima, A. (2005). Building Legal Ontologies with METHONTOLOGY and WebODE, pages 142–157. Springer Berlin Heidelberg, Berlin, Heidelberg.
Dong, H., Hussain, F. K., and Chang, E. (2008). A survey in traditional information retrieval models. In 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, pages 397–402.
Guarino, N., Oberle, D., and Staab, S. (2009). What is an ontology? In Staab, S. and Studer, R., editors, Handbook on Ontologies, International Handbooks on Information Systems, pages 1–17. Springer.
Harman, D. (2019). Information retrieval: The early years. Foundations and Trends in Information Retrieval, 13(5):425–577.
Hersh, W. (2021). Information Retrieval, pages 755–794. Springer International Publishing, Cham.
Lupo, G. and Bailey, J. (2014). Designing and implementing e-justice systems: Some lessons learned from eu and canadian examples. Laws, 3(2):353–387.
Napoleon, S. A. (2013). A survey of web ontology languages and semantic web services. Annals of the Alexandru Ioan Cuza University Economics, 60(1):42–53.
Neman de Novaes, R. and Bissoli, L. G. (2019). Justiça em números: estudos acerca da (in)eficiência do processo judiciário. Revista Vianna Sapiens, 10(1):20.
Savelka, J., Xu, H., and Ashley, K. D. (2019). Improving sentence retrieval from case law for statutory interpretation. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law, ICAIL ’19, page 113–122, New York, NY, USA. Association for Computing Machinery.
Sugathadasa, K., Ayesha, B., de Silva, N., Perera, A. S., Jayawardana, V., Lakmal, D., and Perera, M. (2018). Legal document retrieval using document vector embeddings and deep learning. In Computing Conference 2018.