An Evaluation of Knowledge Base Systems for Large OWL Datasets "In this paper, we present an evaluation of four knowledge base systems (KBS) with respect to use in large OWL applications."
"DLDB-OWL , is a repository for processing, storing, and querying large amounts of OWL data. Its major feature is the extension of a relational database system with description logic inference capabilities. Specifically, DLDBOWL uses Microsoft Access® as the DBMS and FaCT  as the OWL reasoner. It uses the reasoner to precompute subsumption and employs relational views to answer extensional queries based on the implicit hierarchy that is inferred."
"...we were surprised to see that Sesame-Memory could load up to 10 universities, and was able to do it in 5% of the time of the next fastest system. However, for 20 or more universities, Sesame-Memory also succumbed to memory limitations...The result reveals an apparent problem for Sesame-DB: it does not scale in data loading...As an example, it took over 300 times longer to load the 20-university data set than the 1-university data set, although the former set contains only about 25 times more instances than the later...Sesame is a forward-chaining reasoner, and in order to support statement deletions it uses a truth maintenance system to track all deductive dependencies between statements."
"From our analysis, of the systems tested: DLDB is the best for large data sets where an equal emphasis is placed on query response time and completeness."
Lehigh University Benchmark.