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A policy-based B2C e-Contract management workflow methodology using semantic web agents

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Abstract

Since e-Commerce has become a discipline, e-Contracts are acknowledged as the tools that will assure the safety and robustness of the transactions. A typical e-Contract is a binding agreement between parties that creates relations and obligations. It consists of clauses that address specific tasks of the overall procedure which can be represented as workflows. Similarly to e-Contracts, Intelligent Agents manage a private policy, a set of rules representing requirements, obligations and restrictions, additionally to personal data that meet their user’s interests. In this context, this study aims at proposing a policy-based e-Contract workflow management methodology that can be used by semantic web agents, since agents benefit from Semantic Web technologies for data and policy exchanges, such as RDF and RuleML that maximize interoperability among parties. Furthermore, this study presents the integration of the above methodology into a multi-agent knowledge-based framework in order to deal with issues related to rules exchange where no common syntax is used, since this framework provides reasoning services that assist agents in interpreting the exchanged policies. Finally, a B2C e-Commerce scenario is presented that demonstrates the added value of the approach.

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Acknowledgments

NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Kalliopi Kravari & Nick Bassiliades

  2. Data61, CSIRO, NICTA, Spring Hill, Queensland, Australia

    Guido Governatori

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  1. Kalliopi Kravari
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  2. Nick Bassiliades
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  3. Guido Governatori
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Correspondence to Guido Governatori.

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Kravari, K., Bassiliades, N. & Governatori, G. A policy-based B2C e-Contract management workflow methodology using semantic web agents. Artif Intell Law 24, 93–131 (2016). https://doi.org/10.1007/s10506-016-9177-2

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