OwlOntDB: A Scalable Reasoning System for OWL 2 RL Ontologies with Large ABoxes

Ontologies are becoming increasingly important in large-scale information systems such as healthcare systems. Ontologies can represent knowledge from clinical guidelines, standards, and practices used in the healthcare sector and may be used to drive decision support systems for healthcare, as well as store data (facts) about patients. Real-life ontologies may get very large (with millions of facts or instances). The effective use of ontologies requires not only a well-designed and well-defined ontology language, but also adequate support from reasoning tools. Main memory-based reasoners are not suitable for reasoning over large ontologies due to the high time and space complexity of their reasoning algorithms. In this paper, we present OwlOntDB, a scalable reasoning system for OWL 2 RL ontologies with a large number of instances, i.e., large ABoxes. We use a logic-based approach to develop the reasoning system by extending the Description Logic Programs (DLP) mapping between OWL 1 ontologies and datalog rules, to accommodate the new features of OWL 2 RL. We first use a standard DL reasoner to create a complete class hierarchy from an OWL 2 RL ontology, and translate each axiom and fact from the ontology to its equivalent datalog rule(s) using the extended DLP mapping. We materialize the ontology to infer implicit knowledge using a novel database-driven forward chaining method, storing asserted and inferred knowledge in a relational database. We evaluate queries using a modified SPARQL-DL API over the relational database. We show our system performs favourably with respect to query evaluation when compared to two main-memory based reasoners on several ontologies with large datasets including a healthcare ontology.