The cost of reasoning with RDF updates

Research output: ResearchConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 9th IEEE International Conference on Semantic Computing (IEEE ICSC 2015)
Place of PublicationPiscataway, New Jersey, United States
PublisherIEEE
Pages328-331
Number of pages4
ISBN (Print)9781479979356
StatePublished - 6 Feb 2015

    Research areas

  • Resource Description Framework (RDF), reasoning, RDF data model, inference methods, distributed systems, RDF graphs

Bibliographical note

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