Preserving the Alignment of LD with Source Data

Alex Randles, Declan O'Sullivan

Keywords: Usability Testing, Dataset Dynamics, Linked Data, Mappings, Data Quality.

Abstract: A significant proportion of Linked data (LD) is created through mapping of data from a variety of sources of data. LD has been described as highly dynamic in nature with source data being continuously changed, which could impact the quality of the LD data and related mapping artefacts. Changes which have occurred in the source data of the LD datasets should be propagated into the resulting dataset to provide an accurate representation of the underlying data sources. These changes can occur at an extremely fast rate which can results in difficulties propagating each change in a timely manner. Surprisingly, despite the growth of linked data publication on the web of data, there exists no standard to address the dynamics of the data. An approach which captures changes in the source data used by mapping artefacts to create linked data datasets will help to address the dynamics involved in the publication process. Furthermore, capturing changes in a machine-readable format will allow software agents to automatically process them and take appropriate actions to preserve the alignment between mapping artefacts and data sources used to create the LD dataset. Moreover, the ability to monitor the source data and detect changes regularly will support a mechanism to automatically send notifications of changes and potential alignment issues to data producers, therefore, providing necessary information to guide them in improving alignment. Evaluating an approach designed to address the dynamics of linked data is important to provide evidence of sufficient usability. This paper describes the evaluation of the Mapping Quality Improvement (MQI) Framework and focuses on change detection of source data used to create linked data and aims to support data producers in providing timely data to consumers and improving the quality, maintenance and reuse of related mapping artefacts. The evaluation of the MQI framework involved 55 participants with varying levels of background knowledge.