DELTA-LD: A Change Detection Approach for Linked Datasets

Abstract

This paper presents DELTA-LD, an approach that detects and classifies the changes between two versions of a linked dataset. It contributes to the state-of-art: firstly, by proposing a classification to distinctly identify the resources that have had both their IRIs and representation changed and the resources that have had only their IRI changed; secondly by automatically selecting the appropriate resource properties to identify the same resources in different versions of a linked dataset with different IRIs and similar representation. The paper also presents the DELTA-LD change model to represent the detected changes. This model captures the information of both changed resources and triples in linked datasets during its evolution, bridging the gap between resource-centric and triple-centric views of changes. As a result, a single change detection mechanism can support several diverse use cases like interlink maintenance and replica synchronization. The paper, in addition, describes an experiment conducted to examine the accuracy of DELTA-LD in detecting the changes between the person snapshots of DBpedia. The result indicates that the accuracy of DELTA-LD outperforms the state-of-art approaches by up to 4%, in terms of F-measure. It is demonstrated that the proposed classification of changes helped to identify up to 1529 additional updated resources as compared to the existing classification of resource level changes. By means of a case study, we also demonstrate the automatic repair of broken interlinks using the changes detected by DELTA-LD and represented in DELTA-LD change model, showing how 100% of the broken interlinks were repaired between DBpedia person snapshot 3.7 and Freebase.