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. We employ existing frequent pattern mining algorithms for generating new logical rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes intra- and inter-property associations 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.
& Dong, G.
(2012). Towards Logical Linked Data Compression. Proceedings of the Joint Workshop on Large and Heterogeneous Data and Quantitative Formalization in the Semantic Web.