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.

Efficient compliance checking of RDF data

Francesco Pacenza;Jessica Zangari;Francesco Calimeri;
2023-01-01

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.
2023
compliance checking, rdf, data, krr, answer set programming, asp, logic, logics
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/352137
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 2
social impact