The Compression of Indexed Data and Fast Search for Large RDF Graphs

Abstract

In the area of the Semantic Web, RDF datastores are required to search for metadata quickly from large scale RDF data, such as Wikidata and DBpedia in the Linked Open Data (LOD). This paper presents compressed index structures and URI dictionaries of RDF data in order to develop a fast in-memory RDF database system (called FROST). Instead of the complete six types of indexes SPO, SOP, PSO, POS, OSP, and OPS in RDF triples, FROST employs the two types of indexes SPO and OPS that enable us to compactly store RDF triples in the memory. Using the compressed index structures, we develop a fast search method in the datastore system FROST that solves SPARQL queries and returns the query answers from RDF graphs. Our experiments show that (i) FROST outperforms the inmemory RDF frameworks Jena and RDF4J with respect to both fast query processing and saved memory, using the datasets and queries of the LUBM (a benchmarking framework for semantic repositories) and BMDB (RDF Store Benchmarks with DBpedia) benchmarks, and (ii) FROST outperforms the on-disk RDF store Virtuoso with respect to fast query processing, using the LUBM benchmark.