CEDAR: a Fast Taxonomic Reasoner Based on Lattice Operations

Taxonomy classification and query answering are the core reasoning services provided by most of the Semantic Web (SW) reasoners. However, the algorithms used by those reasoners are based on Tableau method or Rules. These well-known methods in the literature have already shown their limitations for large-scale reasoning. In this demonstration, we shall present the CEDAR system for classifying and reasoning on very large taxonomies using a technique based on lattice operations. This technique makes the CEDAR reasoner perform on par with the best systems for concept classification and several orders-ofmagnitude more efficiently in terms of response time for query-answering. The experiments were carried out using very large taxonomies (Wikipedia: 111599 sorts, MESH: 286381 sorts, NCBI: 903617 sorts and Biomodels: 182651 sorts). The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners, namely FaCT++, Pellet, HermiT, TrOWL, SnoRocket and RacerPro.