Abstract
In this paper, we present OntoVAT, a multilingual ontology designed for extracting knowledge in legal judgments related to VAT (Value-Added Tax). This is, to our knowledge, the first extensive ontology in the VAT domain. OntoVAT aims to encapsulate critical concepts in the European VAT area and offers a scalable and reusable knowledge structure to support the automatic identification of VAT-specific concepts in legal texts. Additionally, OntoVAT supports various Artificial Intelligence and Law (AI &Law) tasks, such as extracting legal knowledge, identifying keywords, modeling topics, and extracting semantic relations. Developed using OWL with SKOS lexicalization, OntoVAT’s initial version includes ontological patterns and relations. It is available in three languages, marking a collaborative effort between computer scientists and subject matter experts. In this work, we also present an application scenario where the knowledge encoded within OntoVAT is leveraged in combination with several recent Large Language Models (LLMs). For this application, for which we used the most powerful open source LLMs available today (both generative and non-generative, including legal LLMs), we show the system’s design and some preliminary results.
This works has been supported by the Analytics for Decision of Legal Cases (ADELE), founded by the European Union’s Justice Programme (grant agreement No. 101007420); Davide Liga was supported by the project INDIGO, which is financially supported by the NORFACE Joint Research Programme on Democratic Governance in a Turbulent Age and co-funded by AEI, AKA, DFG and FNR and the European Commission through Horizon 2020 under grant agreement No 822166.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Borgo, S., Masolo, C.: Foundational choices in DOLCE. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 361–381. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_16
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dimou, A., et al.: Airo: an ontology for representing AI risks based on the proposed EU AI act and ISO risk management standards. In: Towards a Knowledge-Aware AI: SEMANTiCS 2022-Proceedings of the 18th International Conference on Semantic Systems, 13-15 September 2022, Vienna, Austria, vol. 55, p. 51. IOS Press (2022)
Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., Schneider, L.: Sweetening ontologies with DOLCE. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 166–181. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45810-7_18
Guarino, N., Welty, C.A.: An overview of OntoClean. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 201–220. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_9
Hitzler, P., Gangemi, A., Janowicz, K.: Ontology engineering with ontology design patterns: foundations and applications, vol. 25. IOS Press (2016)
Hoekstra, R., Breuker, J., Di Bello, M., Boer, A., et al.: The LKIF core ontology of basic legal concepts. LOAIT 321, 43–63 (2007)
Kerremans, K., Temmerman, R., Tummers, J.: Representing multilingual and culture-specific knowledge in a VAT regulatory ontology: support from the termontography method. In: Meersman, R., Tari, Z. (eds.) OTM 2003. LNCS, vol. 2889, pp. 662–674. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39962-9_68
Liga, D., Amitrano, D., Markovich, R.: Patronto, an ontology for patents and trademarks. In: New Frontiers in Artificial Intelligence: JSAI-isAI 2023 Workshops, AI-Biz, EmSemi, SCIDOCA, JURISIN 2023 Workshops, Hybrid Event, June 5–6, 2023, Revised Selected Papers. Springer (2024)
Liga, D., Fidelangeli, A., Markovich, R.: Ontovat, an ontology for knowledge extraction in vat-related judgments. In: New Frontiers in Artificial Intelligence: JSAI-isAI 2023 Workshops, AI-Biz, EmSemi, SCIDOCA, JURISIN 2023 Workshops, Hybrid Event, June 5–6, 2023, Revised Selected Papers. Springer (2024)
Niklaus, J., Matoshi, V., Stürmer, M., Chalkidis, I., Ho, D.E.: Multilegalpile: a 689gb multilingual legal corpus. arXiv preprint arXiv:2306.02069 (2023)
Palmirani, M., Martoni, M., Rossi, A., Bartolini, C., Robaldo, L.: Pronto: privacy ontology for legal compliance. In: Proceedings of 18th European Conference Digital Government (ECDG), pp. 142–151 (2018)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)
Sartor, G., Casanovas, P., Biasiotti, M., Fernández-Barrera, M.: Approaches to Legal Ontologies: Theories, Domains Methodologies. Law. Governance and Technology Series. Springer, Dordrecht (2011)
Temmerman, R., Kerremans, K.: Termontography: ontology building and the sociocognitive approach to terminology description. In: Proceedings of CIL17, vol. 7, p. 1 (2003)
Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liga, D., Fidelangeli, A., Markovich, R. (2024). Using Ontological Knowledge and Large Language Model Vector Similarities to Extract Relevant Concepts in VAT-Related Legal Judgments. In: Bono, M., Takama, Y., Satoh, K., Nguyen, LM., Kurahashi, S. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2023. Lecture Notes in Computer Science(), vol 14644. Springer, Cham. https://doi.org/10.1007/978-3-031-60511-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-60511-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60510-9
Online ISBN: 978-3-031-60511-6
eBook Packages: Computer ScienceComputer Science (R0)