nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Contratto – A Method for Transforming Legal Contracts into Formal Specifications

verfasst von : Michele Soavi, Nicola Zeni, John Mylopoulos, Luisa Mich

Erschienen in: Research Challenges in Information Science

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Legal contracts have been used for millennia to conduct business transactions world-wide. Such contracts are expressed in natural language, and usually come in written form. We are interested in producing formal specifications from such legal text that can be used to formally analyze contracts, also serve as launching pad for generating smart contracts, information systems that partially automate, monitor and control the execution of legal contracts. We have been developing a method for transforming legal contract documents into specifications, adopting a semantic approach where transformation is treated as a text classification, rather than a natural language processing problem. The method consists of five steps that (a) Identify domain terms in the contract and manually disambiguate them when necessary, in consultation with stakeholders; (b) Semantically annotate text identifying obligations, powers, contracting parties, assets and situations; (c) Identify relationships among the concepts mined in (b); (d) Generate a domain model based on the terms identified in (a), as well as parameters and local variables for the contract; (e) Generate expressions that formalize the conditions of obligations and powers using terms identified in earlier steps in a contract specification language. This paper presents the method through an illustrative example, also reports on a prototype implementation of an environment that supports the method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Conceptual Integration for Social-Ecological Systems

Nächstes Kapitel Ontological Foundations for Trust Dynamics: The Case of Central Bank Digital Currency Ecosystems

Specifications describe what a contract does without describing how; they have been used extensively in Computer Science for software, hardware, business processes etc.

eBNF stands for ‘extended Backus-Naur form’ and consists of a notation for specifying programming language grammars.

The full specification can be found at https://legal-analysis.economia.unitn.it:7500/.

Spacy is a free open-source library for Natural Language Processing implementing a neural network for pre-trained models for name entity recognition https://spacy.io/.

Lamport, L.: Who builds a house without drawing blueprints? Commun. ACM 58, 38–41 (2015)

Sharifi, S., Parvizimosaed, A., Amyot, D., Logrippo, L., Mylopoulos, J.: Symboleo: a specification language for Smart Contracts. In: 28th IEEE Requirements Engineering Conference, RE@Next track, Zurich (2020)

Kiyavitskaya, N., Zeni, N., Breaux, T.D., Antón, A.I., Cordy, J.R., Mich, L., Mylopoulos, J.: Automating the extraction of rights and obligations for regulatory compliance. In: 27th International Conference on Conceptual Modelling (ER), pp. 154–168 (2008)

Zeni, N., Kiyavitskaya, N., Mich, L., Cordy, J.R., Mylopoulos, J.: GaiusT: supporting the extraction of rights and obligations for regulatory compliance. Requir. Eng. 20(1), 1–22 (2013). https://doi.org/10.1007/s00766-013-0181-8CrossRef

Zeni, N., Seid, E., Engiels, P., Ingolfo, S., Mylopoulos, J.: NomosT: building large models of law through a tool-supported process. Data Knowl. Eng. (DKE), 117, 407–418 (2018)

Soavi, M., Zeni, N., Mylopoulos, J., Mich, L.: From legal contracts to formal specifications: a progress report. In: Joint Proceedings of REFSQ-2021 Workshops, OpenRE, Posters and Tools Track, and Doctoral Symposium, http://ceur-ws.org, vol. 1613, p. 0073 (2020)

Soavi, M., Zeni, N., Mylopoulos, J., Mich, L.: ContracT – from legal contracts to formal specifications: preliminary results. In: 13th International Working Conference on the Practice of Enterprise Modelling (PoEM) (2020)

Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)CrossRef

Narayana, S., Fillmore, C., Baker, C., Petruck M.: FrameNet meets the semantic web: a DAML+OIL frame representation. In: 18th National Conference on Artificial Intelligence, Edmonton, Alberta (2002)

10.

Clack, C.D.: Languages for Smart and computable contracts. ArXiv preprint arXiv:2104.03764 (2021)

11.

Sennet, A.: Ambiguity. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/fall2021/entries/ambiguity/. Accessed 27 Jan 2022

12.

Mich, L., Garigliano, R.: Ambiguity measures in requirement engineering. In: International Conference on Software Theory and Practice (ICS) (2000)

13.

Kiyavitskaya, N., Zeni, N., Mich, L., Berry, D.M.: Requirements for tools for ambiguity identification and measurement in natural language requirements specifications. Requir. Eng. 13(3), 207–239 (2008)CrossRef

14.

Berry, D.M., Kamsties, E.: Ambiguity in requirements. Perspect. Softw. Requir. 753, 7 (2012)

15.

Quaresma, P., Gonçalves, T.: Using linguistic information and machine learning techniques to identify entities from juridical documents. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS, vol. 6036, pp. 44–59. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12837-0_3

16.

Breaux, T.D., Antón, A.I.: Analyzing goal semantics for rights, permissions, and obligations. In: 13th IEEE International Conference on Requirements Engineering (RE) (2005)

17.

Fantoni, G., Coli, E., Chiarello, F., Apreda, R., Dell’Orletta, F., Pratelli, G.: Text mining tool for translating terms of contract into technical specifications: development and application in the railway sector. Comput. Ind. 124, 103357 (2021)CrossRef

18.

Libal, T., Pascucci, M.: Automated reasoning in normative detachment structures with ideal conditions. In: 17th International Conference on Artificial Intelligence and Law (2019)

19.

Chalkidis, I., Androutsopoulos, I.: A deep learning approach to contract element extraction. JURIX (2017)

20.

Griffo, C., Almeida, J.P.A., Guizzardi, G., Nardi, J.C.: From an ontology of service contracts to contract modeling in enterprise architecture. In: 21st International Enterprise Distributed Object Computing Conference (EDOC), pp. 40–49. IEEE (2017)

21.

Fischbach, J., Frattini, J., Spaans, A., Kummeth, M., Vogelsang, A., Mendez, D., Unterkalmsteiner, M.: Automatic detection of causality in requirement artifacts: the CiRA approach. In: International Working Conference on Requirements Engineering: Foundation for Software Quality, pp. 19–36. Springer, Cham (2021)

22.

Moens, M. F., Boiy, E., Palau, R.M., Reed, C.: Automatic detection of arguments in legal texts. In: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pp. 225–230 (2007)

23.

Sleimi, A., Ceci, M., Sabetzadeh, M., Briand, L. C., Dann, J.: Automated recommendation of templates for legal requirements. In: IEEE 28th International Requirements Engineering Conference (RE), pp. 158–168. IEEE (2020)

24.

Corcho, O., Fernández-López, M., Gómez-Pérez, A., López-Cima, A.: Building legal ontologies with METHONTOLOGY and WebODE. In: Benjamins, V.R., Casanovas, P., Breuker, J., Gangemi, A. (eds.) Law and the Semantic Web. LNCS, vol. 3369, pp. 142–157. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-32253-5_9

25.

Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D.: Integrating a bottom–up and top–down methodology for building semantic resources for the multilingual legal domain. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS, vol. 6036. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12837-0_6

26.

Governatori, G., Milosevic, Z.: A formal analysis of a business contract language. Int. J. Coop. Inf. Syst. 15(04), 659–685 (2006)CrossRef

27.

Dwivedi, V., Norta, A., Wulf, A., Leiding, B., Saxena, S., Udokwu, C.: A formal specification smart-contract language for legally binding decentralized autonomous organizations. IEEE Access 9, 76069–76082 (2021)

28.

He, X., Qin, B., Zhu, Y., Chen, X., Liu, Y.: SPESC: a specification language for smart contracts. In: 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 1, pp. 132–137. IEEE (2018)

29.

Maxwell, J.C., Antón, A.I.: Developing production rule models to aid in acquiring requirements from legal texts. In: 17th IEEE International Requirements Engineering Conference. IEEE (2009)

30.

Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the muppets straight out of law school. ArXiv [cs.CL] (2020)

31.

Koreeda, Y., Manning, C.D.: ContractNLI: a dataset for document-level natural language inference for contracts. ArXiv [cs.CL] (2021)

32.

Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. ArXiv [cs.CL] (2019)

Titel: Contratto – A Method for Transforming Legal Contracts into Formal Specifications
verfasst von: Michele Soavi
Nicola Zeni
John Mylopoulos
Luisa Mich
Verlag: Springer International Publishing
Buch: Research Challenges in Information Science
Print ISBN: 978-3-031-05759-5

Electronic ISBN: 978-3-031-05760-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-05760-1_20

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"