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This project will examine the area of trust on the Semantic Web and develop a framework for publishing and verifying trusted Linked Data. Linked Data describes a method of publishing structured data, automatically readable by computers, which can linked to other heterogeneous data with the purpose of becoming more useful. Trust plays a significant role in the adoption of new technologies and even more so in a sphere with such vast amounts of publicly-created data. Trust is paramount to the effective sharing and communication of tacit knowledge (Hislop, 2013). Up to now, the area of trust in Linked Data has not been adequately addressed, despite the Semantic Web stack having included a trust layer from the very beginning (Artz and Gil, 2007). Some of the most accurate data on the Semantic Web lies practically unused, while some of the most used linked data has high numbers of errors (Zaveri et al., 2013). Many of the datasets and links that exist on the Semantic Web are out of date and/or invalid and this undermines the credibility and validity, and ultimately, the trustworthiness of both the dataset and the data provider (Rajabi et al., 2012). This research will examine a number of datasets to determine the quality metrics that a dataset is required to meet to be considered ‘trusted’. The key findings will be assessed and utilized in the creation of a learning tool and a framework for creating trusted Linked Data.
Clarke, P. : A framework for the analysis and user-driven evaluation of trust on the semantic web, Masters Dissertation, Dublin Institute of Technology, 2014.