  Linked Datasets (LDs) are constantly evolving and the applications using a
Linked Dataset (LD) may face several issues such as outdated data or broken
interlinks due to evolution of the dataset. To overcome these issues, the
detection of changes in LDs during their evolution has proven crucial. As LDs
evolve frequently, the change detection during the evolution should also be
done at frequent intervals. However, due to limitation of available
computational resources such as capacity to fetch data from LD and time to
detect changes, the frequent change detection may not be possible with existing
change detection techniques. This research proposes to explore the notion of
prioritization of regions (subsets) in LDs for change detection with the aim of
achieving optimal accuracy and efficient use of available computational
resources. This will facilitate the detection of changes in an evolving LD at
frequent intervals and will allow the applications to update their data closest
to real-time data.
