We describe our method for benchmarking Semantic Web knowledge base systems with respect to use in large OWL applications. We present the Lehigh University Benchmark (LUBM) as an example of how to design such benchmarks. The LUBM features an ontology for the university domain, synthetic OWL data scalable to an arbitrary size, fourteen extensional queries representing a variety of properties, and several performance metrics. The LUBM can be used to evaluate systems with different reasoning capabilities and storage mechanisms. We demonstrate this with an evaluation of two memorybased systems and two systems with persistent storage.

We describe our method for benchmarking Semantic Web knowledge base systems with respect to use in large OWL applications. We present the Lehigh University Benchmark (LUBM) as an example of how to design such benchmarks. The LUBM features an ontology for the university domain, synthetic OWL data scalable to an arbitrary size, fourteen extensional queries representing a variety of properties, and several performance metrics. The LUBM can be used to evaluate systems with different reasoning capabilities and storage mechanisms. We demonstrate this with an evaluation of two memorybased systems and two systems with persistent storage.

Functional characterization of disease-causing variants at risk loci has been a significant challenge. Here we report a high-throughput single-nucleotide polymorphisms sequencing (SNPs-seq) technology to simultaneously screen hundreds to thousands of SNPs for their allele-dependent protein-binding differences. This technology takes advantage of higher retention rate of protein-bound DNA oligos in protein purification column to quantitatively sequence these SNP-containing oligos. We apply this technology to test prostate cancer-risk loci and observe differential allelic protein binding in a significant number of selected SNPs. We also test a unique application of self-transcribing active regulatory region sequencing (STARR-seq) in characterizing allele-dependent transcriptional regulation and provide detailed functional analysis at two risk loci (RGS17 and ASCL2). Together, we introduce a powerful high-throughput pipeline for large-scale screening of functional SNPs at disease risk loci.

Abstract. In this paper, we present our work on evaluating knowledge base systems with respect to use in large OWL applications. To this end, we have developed the Lehigh University Benchmark (LUBM). The benchmark is intended to evaluate knowledge base systems with respect to extensional queries over a large dataset that commits to a single realistic ontology. LUBM features an OWL ontology modeling university domain, synthetic OWL data generation that can scale to an arbitrary size, fourteen test queries representing a variety of properties, and a set of performance metrics. We describe the components of the benchmark and some rationale for its design.Based on the benchmark, we have conducted an evaluation of four knowledge base systems (KBS). To our knowledge, no experiment has been done with the scale of data used here. The smallest dataset used consists of 15 OWL files totaling 8MB, while the largest dataset consists of 999 files totaling 583MB. We evaluated two memory-based systems (OWLJessKB and memory-based Sesame) and two systems with persistent storage (database-based Sesame and DLDB-OWL). We show the results of the experiment and discuss the performance of each system. In particular, we have concluded that existing systems need to place a greater emphasis on scalability.

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