Logical Linked Data Compression

Citation data:

The Semantic Web: Semantics and Big Data, ISSN: 0302-9743, Vol: 7882, Page: 170-184

Publication Year:
2013
Usage 137
Downloads 127
Abstract Views 10
Citations 11
Citation Indexes 11
Repository URL:
https://corescholar.libraries.wright.edu/cse/71
DOI:
10.1007/978-3-642-38288-8_12; 10.1007/978-3-642-38288-8_12.
Author(s):
Joshi, Amit Krishna; Hitzler, Pascal; Dong, Guozhu
Publisher(s):
Springer Nature
Tags:
Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Social and Behavioral Sciences
book chapter description
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.