LUBM: A benchmark for OWL knowledge base systems

By Yuanbo Guo, Zhengxiang Pan, Jeff Heflin. Journal of Web Semantics, 2005

1,611 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/aeedd9f5c0fe4ca3e850cfbf7425e3f312e30cf7

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Citations in the corpus

Diversified Stress Testing of RDF Data Management Systems (2014, methodology): “– The Lehigh University Benchmark (LUBM) [12] was originally designed for testing the inferencing capabilities of Semantic Web repositories.”
The Berlin SPARQL Benchmark (2009, methodology): “The LUBM benchmark has been extended in (Ma et al., 2006) to the University Ontology Benchmark (UOBM) by adding axioms that make use of all OWL Lite and OWL DL constructs.”
DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data (2011, methodology): “LUBM uses an artificial data generator, which generates synthetic data for universities, their departments, their professors, employees, courses and publications.”
Deep Learning for Ontology Reasoning (2017, methodology): “s offourSemantic Web KBs ofdifferentsizes andcharacteristics. Amongthese are two real-world datasets, a fraction of DBpedia (Bizer et al., 2009) and the Claros KB3, as well as two syntheticones,LUBM (Guo et al., 2005) andUOBM (Ma et al., 2006). Theircharacteristics are summarized in Table 1. While a”
Ontology Alignment Evaluation Initiative: Six Years of Experience (2011, methodology): “This means that besides the efﬁciency, completeness and correctness of the underlying reasoning procedures are of a major importance and were also considered in the respective benchmarks, see e.g., [ 33; 44 ] .”
Deep learning for noise-tolerant RDFS reasoning (2019, background): “Ground-Truthing in LUBM1 Lehigh University Benchmark (LUBM) [30] is a benchmark for Semantic Web repositories.”
Pellet: A practical OWL-DL reasoner (2007, methodology): “For this experiment, we have used the data and queries included in the Lehigh University Benchmark (LUBM) [37].”
Scalable SPARQL querying of large RDF graphs (2011, methodology): “F.1 Varying Number of Machines To explore the performance of our system as we vary the number of machines in the cluster, we run LUBM (2000) on clusters with 1, 5, 10, and 20 worker machines.”
TriAL (2018, background): “Most well known SPARQL benchmarks do not include queries with property paths (see, e.g., LUBM [30] or BSBM [13]).”
SP^2Bench: A SPARQL Performance Benchmark (2008, methodology): “As a proof of concept, most of these approaches have been evaluated experimentally either in user-deﬁned scenarios, on top of the LUBM benchmark [18], or using the Barton Library benchmark [19].”
RDF Support in the Virtuoso DBMS (2007, neutral): “[4] Guo, Y.”