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Massive RDF data sets are becoming commonplace. RDF data is typicallygenerated in social semantic domains (such as personal information management)wherein a fixed schema is often not available a priori. We propose a simpleThree-way Triple Tree (TripleT) secondary-memory indexing technique tofacilitate efficient SPARQL query evaluation on such data sets. The novelty ofTripleT is that (1) the index is built over the atoms occurring in the dataset, rather than at a coarser granularity, such as whole triples occurring inthe data set; and (2) the atoms are indexed regardless of the roles (i.e.,subjects, predicates, or objects) they play in the triples of the data set. Weshow through extensive empirical evaluation that TripleT exhibits multipleorders of magnitude improvement over the state of the art on RDF indexing, interms of both storage and query processing costs.

A role-free approach to indexing large RDF data sets in secondary memory for efficient SPARQL evaluation