Semantic Business Process Regulatory Compliance Checking Using LegalRuleML

Governatori, Guido; Hashmi, Mustafa; Lam, Ho-Pun; Villata, Serena; Palmirani, Monica

doi:10.1007/978-3-319-49004-5_48

Cited by 27 publications

(20 citation statements)

References 19 publications

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“…The experiments are based on the Telecommunication Consumer Protections Code described in Governatori et al (2016) which consists of 6 constitutive statements, 78 prescriptive statements, and 10 override statements, and transformed into a defeasible theory with 6 strict rules, 78 defeasible rules, and 10 superiority relations (with 121 literals). In order to further evaluate the scalability of the LegalRuleML theory parser and renderer, we have created a set of synthetic theories by duplicating the set of (all) statements in the original theory and renamed their keys.…”

Section: Methodsmentioning

confidence: 99%

Enabling reasoning with LegalRuleML

Lam¹,

Hashmi²

2018

Theory and Practice of Logic Programming

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In order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using legalruleml to a variant of modal defeasible logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client's preferences.

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Section: Methodsmentioning

confidence: 99%

Enabling reasoning with LegalRuleML

Lam¹,

Hashmi²

2018

Theory and Practice of Logic Programming

Self Cite

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“…This provides an important step in the automation of legal compliance by enabling machine-readable and queryable information regarding applicable standards for a specific legal clause. Furthermore, existing work also addresses the requirements of metadata (Wenning & Kirrane, 2018) and standardisation of legal notation associated with compliance (Governatori, Hashmi, Lam, Villata, & Palmirani, 2016).…”

Section: Emerging Effortsmentioning

confidence: 99%

Standardisation, Data Interoperability, and GDPR

Pandit

Debruyne

O’Sullivan

et al. 2020

Shaping the Future Through Standardization

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The General Data Protection Regulation (GDPR) has changed the ecosystem of services involving personal data and information. It emphasises several obligations and rights, amongst which the Right to Data Portability requires providing a copy of the given personal data in a commonly used, structured, and machine-readable format – for interoperability. The GDPR thus explicitly motivates the use and adoption of data interoperability concerning information. This chapter explores the entities and their interactions in the context of the GDPR to provide an information model for the development of interoperable services. The model categorises information and exchanges and explores existing standards and efforts towards use for interoperable interactions. The chapter concludes with an argument for the use and adoption of structured metadata to enable more expressive services through semantic interoperability.

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“…In addition, there is a body of work relating to legal reasoning [2,11,18,18,22,29]. Palmirani et al [29] and Athan et al [2] demonstrate how LegalRuleML, an extension of RuleML [7], can be used to specify legal norms, guidelines, and policies.…”

Section: Related Workmentioning

confidence: 99%

“…While, Lam and Hashmi [22] demonstrate how LegalRuleML can be translated into defeasible logic, which allows for modelling and reasoning over business policies. Governatori et al [18] in turn shows how LegalRuleML together with Semantic technologies is used for business process regulatory compliance checking.…”

Section: Related Workmentioning

confidence: 99%

“…In order to support automated compliance checking it is necessary to encode both GDPR requirements and business processes in a machine understandable format. Existing work in the area focuses on modelling legal requirements using semantic technologies [4,5,6,31]; reasoning over legal rules [2,11,29]; and reasoning over business policies [11,18,22]. At the same time there are several semantic technology based general policy languages, such as KAoS [9], Rei [21] and Protune [8], which could potentially be used to model and reason over legal requirements and business policies.…”

Section: Introductionmentioning

confidence: 99%

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ODRL Policy Modelling and Compliance Checking

Vos

Kirrane

Padget

et al. 2019

Lecture Notes in Computer Science

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This paper addresses the problem of constructing a policy pipeline that enables compliance checking of business processes against regulatory obligations. Towards this end, we propose an Open Digital Rights Language (ODRL) profile that can be used to capture the semantics of both business policies in the form of sets of required permissions and regulatory requirements in the form of deontic concepts, and present their translation into Answer Set Programming (via the Institutional Action Language (InstAL)) for compliance checking purposes. The result of the compliance checking is either a positive compliance result or an explanation pertaining to the aspects of the policy that are causing the noncompliance. The pipeline is illustrated using two (key) fragments of the General Data Protect Regulation, namely Articles 6 (Lawfulness of processing) and Articles 46 (Transfers subject to appropriate safeguards) and industrially-relevant use cases that involve the specification of sets of permissions that are needed to execute business processes. The core contributions of this paper are the ODRL profile, which is capable of modelling regulatory obligations and business policies, the exercise of modelling elements of GDPR in this semantic formalism, and the operationalisation of the model to demonstrate its capability to support personal data processing compliance checking, and a basis for explaining why the request is deemed compliant or not.

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Semantic Business Process Regulatory Compliance Checking Using LegalRuleML

Cited by 27 publications

References 19 publications

Enabling reasoning with LegalRuleML

Enabling reasoning with LegalRuleML

Standardisation, Data Interoperability, and GDPR

ODRL Policy Modelling and Compliance Checking

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