A Language for Defining and Detecting Interrelated Complex Changes on RDF(S) Knowledge Bases
In 18th International Conference on Enterprise Information Systems (ICEIS 2016), 25-28 April 2016
Abstract. The dynamic nature of web data brings forward the need for maintaining data versions as well as identifying changes between them. In this paper, we deal with problems regarding understanding evolution, focusing on RDF(S) knowledge bases, as RDF is a de-facto standard for representing data on the web. We argue that revisiting past snapshots or the differences between them is not enough for understanding how and why data evolved. Instead, changes should be treated as first-class-citizens. In our view, this involves supporting semantically rich, user-defined changes that we call complex changes, as well as identifying the interrelations between them. In this paper, we present our perspective regarding complex changes, propose a declarative language for defining complex changes for RDF(S) knowledge bases, and show how this language is used to detect complex change instances among dataset versions.