This paper presents the DELTA-R approach that detects and
classifies the changes between two versions of a linked dataset. It contributes
to the state of the art firstly: by proposing a more granular classification of
the resource level changes, and secondly: by automatically selecting the
appropriate resource properties to identify the same resources in different
versions of a linked dataset with different URIs and similar representation.
The paper also presents the DELTA-R change model to represent the
changes detected by the DELTA-R approach. This model bridges the gap
between resource-centric and triple-centric views of changes in linked
datasets. As a result, a single change detection mechanism will be able to
support the use cases like interlink maintenance and dataset or replica
synchronization. Additionally, the paper describes an experiment conducted
to examine the accuracy of the DELTA-R approach in detecting the changes
between two versions of a linked dataset. The result indicates that the
accuracy of DELTA-R approach outperforms the state of the art approaches
by up to 4%. It is demonstrated that the proposed more granular
classification of changes helped to identifyup to 1529 additional updated
resources compered to X.By means of a case study, we demonstrate the
support of DELTA-R approach and change model for an interlink
maintenance use case. The result shows that 100% of the broken interlinks
were repaired between DBpedia person snapshot 3.7 and Freebase
