Explaining Data Patterns Using Background Knowledge from Linked Data

When using data mining to find regularities in data, the obtained results (or patterns) need to be interpreted. The explanation of such patterns is achieved using the background knowledge which might be scattered among different sources. This intensive process is usually committed to the experts in the domain. With the rise of Linked Data and the increasing number of connected datasets, we assume that the access to this knowledge can be easier, faster and more automated. This PhD research aims to demonstrate whether Linked Data can be used to provide the background knowledge for pattern interpretation and how.