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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Logical Linked Data Compression
verfasst von : Amit Krishna Joshi, Pascal Hitzler, Guozhu Dong
Erschienen in: The Semantic Web: Semantics and Big Data
Verlag: Springer Berlin Heidelberg
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Linked data has experienced accelerated growth in recent years. With the continuing proliferation of structured data, demand for RDF compression is becoming increasingly important. In this study, we introduce a novel lossless compression technique for RDF datasets, called Rule Based Compression (RB Compression) that compresses datasets by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes semantic associations present in the RDF graph. Depending on the nature of the dataset, our system is able to prune more than 50% of the original triples without affecting data integrity.