The semantic web uses RDF as its data model, providing ultimate flexibility for users to represent and evolve data without need of a schema. Yet, this flexibility poses challenges in implementing efficient RDF stores, leading from plans with very many self-joins to a triple table, difficulties to optimize these, and a lack of data locality since without a notion of multi-attribute data structure, clustered indexing opportunities are lost. Apart from performance issues, users of huge RDF graphs often have problems formulating queries as they lack any system-supported notion of the structure in the data. In this research, we exploit the observation that real RDF data, while not as regularly structured as relational data, still has the great majority of triples conforming to regular patterns. We conjecture that a system that would recognize this structure automatically would both allow RDF stores to become more efficient and also easier to use. Concretely, we propose to derive self-organizing RDF that stores data in PSO format in such a way that the regular parts of the data physically correspond to relational columnar storage; and propose RDFscan/RDFjoin algorithms that compute star-patterns over these without wasting effort in self-joins. These regular parts, i.e. tables, are identified on ingestion by a schema discovery algorithm -- as such users will gain an SQL view of the regular part of the RDF data. This research aims to produce a state-of-the-art SPARQL frontend for MonetDB as a by-product.
§ Minh-Duc, Pham, Boncz, P.A., " Self-organizing Structured RDF in MonetDB ," PhD Symposium, ICDE, 2013. (pdf)