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Efficient compliance checking of RDF data
,
Francesco Pacenza,
Jessica Zangari,
Roberta Calegari
Journal of Logic and Computation
Swansea University Author:
Livio Robaldo
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DOI (Published version): 10.1093/logcom/exad034
Abstract
Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the...
| Published in: | Journal of Logic and Computation |
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| ISSN: | 0955-792X 1465-363X |
| Published: |
Oxford University Press (OUP)
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63627 |
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| Abstract: |
Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the legal domain (LegalTech). The authors of this paper deem as crucial the research and implementation of compliance checkers that can directly process data in RDF format, as nowadays more and more (big) data in this format are becoming available worldwide, across a multitude of different domains. Among the automated technologies that have been used in recent literature, to the best of our knowledge, only two of them have been evaluated with input states of affairs encoded in RDF format. This paper formalizes a selected use case in these two technologies and compares the implementations, also in terms of simulations with respect to shared synthetic datasets. |
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| Keywords: |
Automated compliance checking, AI technologies, RDF data |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Livio Robaldo has been supported by the Legal Innovation Lab Wales operation within Swansea University's Hillary Rodham Clinton School of Law. The operation has been part-funded by the European Regional Development Fund through the Welsh Government. Francesco Calimeri, Francesco Pacenza, and Jessica Zangari acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU, and the support of the project PRIN PE6, Title: Declarative Reasoning over Streams'', funded by the Italian Ministero dell'Università, dell'Istruzione e della Ri cerca (MIUR), CUP:H24I17000080001. The research of Roberta Calegari has been partially supported by the CompuLaw'' project, funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 833647). |