Resource Description Framework (RDF) data represents information linkage around the Internet. It uses Internationalized Resources Identifier (IRI) which can be referred to external information. Typically, an RDF data is serialized as a large text file which contains millions of relationships. In this work, we propose a framework based on TripleID-Q, for query processing of large RDF data in a GPU. The key elements of the framework are 1) a compact representation suitable for a Graphics Processing Unit (GPU) and 2) its simple representation conversion method which optimizes the preprocessing overhead. Together with the framework, we propose parallel algorithms which utilize thousands of GPU threads to look for specific data for a given query as well as to perform basic query operations such as union, join, and filter. The TripleID representation is smaller than the original representation 3-4 times. Querying from TripleID using a GPU is up to 108 times faster than using the traditional RDF tool. The speedup can be more than 1,000 times over the traditional RDF store when processing a complex query with union and join of many subqueries.

In the Semantic Web, the Resource Description Framework (RDF) has become the standard representation to describe Internet resources. RDF data is structured in triples comprising a subject, a predicate and an object: the predicate defines the relation between subject and object. The RDF Schema (RDFS) extends raw RDF data with a standardized vocabulary to allow for entailment, e.g., type inheritance or type inference. Processing large RDF data sets, which are commonly stored as text files, is a time-intensive task: querying and entailment on RDF data requires a huge amount of computational power and storage. We propose TripleID – a framework for RDF querying and entailment processing. TripleID provides a novel, compressed file format for RDF data and utilizes Graphics Processing Units (GPUs) for accelerated, highly parallelized data processing. We demonstrate the advantages of our framework on real-world RDF data: TripleID reduces storage size for RDF data by up to 75% and accelerates querying and entailment processing up to 40 times as compared to the state-of-the-art tools that use conventional CPUs.

Nowadays, semantic web technology depends on interexchange and integration of RDF data for various aspects of each social communication. The searching for possible answer to the related topics across the open data source obviously becomes a massive task. In this research, we study the RDF query with parallel processing on the GPU. In particular, in this paper, we consider the compact data representation for the RDFs which can enable importing more data to the GPUs memory to enable parallel search in the GPU. The key idea is the use of compressed data type such as HDT before going the search on GPUs. Loading the HDT file to the GPUs straightforwardly and perform searching may not be the good solutions. Thus, this work presents the tree representation from the HDT data which can com-pact the HDT triples, ease the GPU memory transfer, and enable the GPU parallel search. With the HDT representation, the size is reduced from the original RDF about 10%-30%. Together with the tree array representation, we can reduce the redundant terms from in HDT triples by 30%-50% for the test cases.