A Flexible Framework for Understanding the Dynamics of Evolving RDF Datasets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9366)


The dynamic nature of Web data gives rise to a multitude of problems related to the description and analysis of the evolution of RDF datasets, which are important to a large number of users and domains, such as, the curators of biological information where changes are constant and interrelated. In this paper, we propose a framework that enables identifying, analysing and understanding these dynamics. Our approach is flexible enough to capture the peculiarities and needs of different applications on dynamic data, while being formally robust due to the satisfaction of the completeness and unambiguity properties. In addition, our framework allows the persistent representation of the detected changes between versions, in a manner that enables easy and efficient navigation among versions, automated processing and analysis of changes, cross-snapshot queries (spanning across different versions), as well as queries involving both changes and data. Our work is evaluated using real Linked Open Data, and exhibits good scalability properties.


Change Detection Complex Change Simple Change SPARQL Query Triple Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer ScienceFORTHHeraklionGreece
  2. 2.Institute for the Management of Information SystemsATHENAAthensGreece

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