Challenges of Open Data Quality : More Than Just License, Format, and Customer Support

The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, award reference: EP/G066051/1; and by the Innovate UK award reference: 102615.Peer reviewedPostprin

Social Media Data in Research : Provenance Challenges

The work described here was funded by a grant from the United Kingdom’s Economic and Social Research Council Social Media - Developing Understanding, Infrastructure & Engagement (ES/M001628/1).Postprin

Provenance on the Web, Leaving the Walled Garden Behind ...

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SC-PROV: A Provenance Vocabulary for Social Computation

The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Postprin

Utilising Provenance to Enhance Social Computation

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The Transport Disruption Ontology

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Assessing the Quality of Semantic Sensor Data

Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Publisher PD

Managing the Provenance of Crowdsourced Disruption Reports

A paid open access option is available for this journal. Authors own final version only can be archived Publisher's version/PDF cannot be used On author's website immediately On any open access repository after 12 months from publication Published source must be acknowledged Must link to publisher version Set phrase to accompany link to published version (see policy) Articles in some journals can be made Open Access on payment of additional chargePublisher PD

Capturing the Provenance of Internet of Things Deployments

The research described here is supported by the RCUK Digital Economy programme award made to the University of Aberdeen; award reference: EP/N028074/1.Postprin

Minimality and simplicity of rules for the internet-of-things.

Rule-based systems have been increasing in popularity in recent years. They allow for easier handling of both simple and complicated problems utilising a set of rules created in various ways (e.g., manually, or (semi-) automatically, via, say, machine learning or decision trees) depending on the situation. Despite their usefulness however, there are still improvements to be made. Knowledge representation technologies have been available for a long time and provide the means to represent domains formally and correlate entities in those domains. They also allow for ontological reasoning that can take advantage of such connections between entities. These techniques can be useful when applied on rule-based systems in order to improve the quality of rules and, hence, overall system performance. We describe and implement an approach to refine rules used in Internet-of-Things scenarios using knowledge representation and reasoning. The proposed solution uses ontological reasoning on the preconditions and postconditions of rules as it aims to reduce the total amount of rules in a system and simplify them