Abstract: The recent media focus on Smart City services, particularly ride sharing, that provide ordinary users with the ability to advertise their resources has highlighted society’s need for transparent and accountable systems. Current systems offer little transparency behind their processes that claim to provide accountability to and for their users. To address such a concern, some applications provide a static, textual description of the automated algorithms used, with a view to promote transparency. However, this is not sufficient to inform users exactly how information is derived. These descriptions can be enhanced by explaining the actual execution of the algorithm, the data it operated on, and the parameters it was configured with. Such descriptions about a system’s execution and its information flow can be expressed using PROV, a standardised provenance data model. However, given its generic and domain-agnostic nature, PROV only provides limited information about the relationship between provenance elements. Combined with semantic information, a PROV instance becomes a rich resource, which can be exploited to provide users with understandable accounts of automated processes, thereby promoting transparency and accountability. Thus, this paper contributes, a vocabulary for Smart City resource sharing applications, an architecture for accountable systems, and a set of use cases that demonstrate and quantify how the semantics enrich an account in a ride share scenario.

Citation: Heather Packer, Dimitris Diochnos, Michael Rovatsos, Ya’akov Gal, Luc Moreau, Semantics and Provenance for Accountable Smart City Applications, The Role of Semantics in Smart Cities, Semantic Web Journal special issue, 2014.

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The ESSENCE – Marie Curie Initial Training Network (FP7-PEOPLE-2013-ITN) is a 4-year, €4 million programme funded by the European Commission (grant agreement no. 607062) and started on the 1st November 2013.

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