Picture map of Europe with pins indicating European capital cities

Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

Explore research outputs by the European Policies Research Centre...

The cost of reasoning with RDF updates

Al Azwari, Sana and Wilson, John N. (2015) The cost of reasoning with RDF updates. In: Proceedings of the 9th IEEE International Conference on Semantic Computing (IEEE ICSC 2015). IEEE, Piscataway, New Jersey, United States, pp. 328-331. ISBN 9781479979356

[img]
Preview
PDF (AlAzwari-Wilson-ICSC2015-the-cost-of-reasoning-with-RDF-triples)
AlAzwari_Wilson_ICSC2015_the_cost_of_reasoning_with_RDF_triples.pdf - Accepted Author Manuscript

Download (1MB) | Preview

Abstract

Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment. Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing.