On the Feasibility of Crawling Linked Data Sets for Reusable Defect Corrections

Current linked open data standards have encouraged the publication of a large number of data sets on the public Web. While some data providers put a lot of energy and resources into maintaining high quality data, others do not, meaning that the quality of the data in many LOD sources is variable and unpredictable. This makes the construction of novel applications on top of the data more difficult and expensive than it otherwise would be. However, these same data standards also open up possibilities for new ways of managing information quality (IQ). In this paper, we propose one such approach, the IQ-bot, and present the results of our study of its feasibility. An IQbot is a 3rd party component that crawls the Web of data, looking for changes that have been made to data sets, and inferring from them where a correction to a data defect has been made. These corrections can then potentially be made available for application to other databases showing evidence of the presence of the same data defect. In this way, the benefits of the curation effort put into a small number of data sets can be propagated throughout the Web of data.