Information Management: Pay-as-you-go data integration for linked data

Linked Data (LD) provides principles for publishing data that underpin the development of an emerging web of data. LD follows the web in providing low barriers to entry: publishers can make their data available using a small set of standard technologies, and consumers can search for and browse published data using generic tools. Like the web,
consumers frequently consume data in broadly the form in which it was published; this will be satisfactory in some cases, but the diversity of publishers means that the data
required to support a task may be stored in many different sources, and described in many different ways. As such, although RDF provides a syntactically homogeneous language for describing data, sources typically manifest a wide range of heterogeneities, in terms of how data on a concept is represented.

Many aspects of both publication and consumption of LD stand to benefit from a pay-as-you-go approach to data integration; the scale and dynamic nature of the web of data make manual or up-front data integration impractical. Research in this area could address topics such as: pay-as-you-go publication, data integration for enhanced browsing and querying, and data quality in linked data.

PhD projects in this area at Manchester could involve Norman Paton, Alvaro Fernandes and Suzanne Embury and Bijan Parsia, who work on different challenges and techniques. For initial enquiries, please approach Norman Paton ([Email Address Removed]).