As the development of IT and scientific technology, very large amounts of knowledge data are continuously being created and the big data era can be said to have arrived. Therefore, RDF store inserting and inquiring into knowledge bases has to be scaled up in order to deal with such large sources of data. To this end, we propose a scalable distributed RDF store based on a distributed database that uses bulk-loading for billions of triples to store data and to respond to user queries quickly. In order to achieve this purpose, we introduce a bulk-loading algorithm using the MapReduce framework and the SPARQL query processing engine to connect to a large distributed database. Experimental results show that the proposed bulk-loading algorithm achieves 67.893K triples per second to load approximately 33 billion triples. Therefore, the experiment proves proposed RDF store can manage billions of triples scale data.
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