Linked Data - A study of how to extract data into a machine readable format by using semantic web technologies

Abstract

The effort to transform and extend data is a growing business in many industries. Proprietary data formats and inconsistent data structures create complexity for machines to understand these formats, and each new dataset needs human attention in order for it to work in a system. This study investigates how data can be transformed into a machine understandable format, and make it possible to link and access objects on the web by giving them unique references. Semantic web technologies and linked data have been adopted to investigate this procedure. The investigation was done by means of the research method of laboratory experiments. A real world example was created from example data provided by AstraZeneca R&D and the organization CDISC. Tests were executed against this example environment to examine the theories behind the semantic web and linked data. The study shows that data can be parsed into a structured, machine readable, graph data structure with RDF and OWL. The structure can easily be extended. The converted objects can in this new data format be linked to from other repositories of data. Intelligent queries can also be executed against the new data with SPARQL.