TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFs

Resource Description Framework (RDF) is a commonly used format for semantic web processing. It basically contains strings representing terms and their relationships which can be queried or inferred. RDF is usually a large text file which contains many million relationships. In this work, we propose a framework, TripleID, for processing queries of large RDF data. The framework utilises Graphics Processing Units (GPUs) to search RDF relations. The RDF data is first transformed to the encoded form suitable for storing in the GPU memory. Then parallel threads on the GPU search the required data. We show in the experiments that one GPU on a personal desktop can handle 100 million triple relations, while a traditional RDF processing tool can process up to 10 million triples. Furthermore, we can query sample relations within 0.18 s with the GPU in 7 million triples, while the traditional tool takes at least 6 s for 1.8 million triples.