A Graph-Based Approach for Classifying OWL 2 QL Ontologies

ESWC, 2013

Ontology classification is the reasoning service that computes all subsumption relationships inferred in an ontology between concept, role, and attribute names in the ontology signature. OWL 2 QL is a tractable profile of OWL 2 for which ontology classification is polynomial in the size of the ontology TBox. However, to date, no efficient methods and implementations specifically tailored to OWL 2 QL ontologies have been developed. In this paper, we provide a new algorithm for ontology classification in OWL 2 QL, which is based on the idea of encoding the ontology TBox into a directed graph and reducing core reasoning to computation of the transitive closure of the graph. We have implemented the algorithm in the QuOnto reasoner and extensively evaluated it over very large ontologies. Our experiments show that QuOnto outperforms various popular reasoners in classification of OWL 2 QL ontologies.

2012

Abstract. Classification is a fundamental reasoning task in ontology design, and there is currently a wide range of reasoners highly optimised for classification of OWL 2 ontologies. There are also several reasoners that are complete for restricted fragments of OWL 2, such as the OWL 2 EL profile. These reasoners are much more efficient than fully-fledged OWL 2 reasoners, but they are not complete for ontologies containing (even if just a few) axioms outside the relevant fragment.

Web Semantics: Science, …, 2012

Ontology classification-the computation of the subsumption hierarchies for classes and properties-is a core reasoning service provided by all OWL reasoners known to us. A popular algorithm for computing the class hierarchy is the so-called Enhanced Traversal (ET) algorithm. In this paper we present a new classification algorithm that attempts to address certain shortcomings of ET and improve its performance. Apart from classification of classes, we also consider object and data property classification. Using several simple examples, we show that the algorithms commonly used to implement these tasks are incomplete even for relatively weak ontology languages. Furthermore, we show that property classification can be reduced to class classification, which allows us to classify properties using our optimised algorithm. We implemented all our algorithms in the OWL reasoner HermiT. The results of our performance evaluation show significant performance improvements on several well-known ontologies.

2012

Abstract. Ontology Based Data Access (OBDA) has drawn considerable attention from the OWL and RDF communities. In OBDA, instance data is accessed by means of mappings, which state the relationship between the data in a data source (eg, an RDBMSs) and the vocabulary of an ontology. In this paper we present Quest, a new system for OBDA focused on fast and efficient reasoning with large ontologies and large volumes of data.

Lecture Notes in Computer Science, 2013

Ontologies are becoming increasingly important in largescale 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. Reallife 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.

Lecture Notes in Computer Science, 2014

This paper addresses the first release of the Rule-based Query Answering and Reasoning framework (RuQAR). The tool provides the ABox reasoning and query answering with OWL 2 RL ontologies executed by forward chaining rule reasoners. We describe current implementation and an experimental evaluation of RuQAR by performing reasoning on the number of benchmark ontologies. Additionally, we compare obtained results with inferences provided by HermiT and Pellet. The evaluation shows that we can perform the ABox reasoning with considerably better performance than DL-based reasoners.

2011

This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that succeed in classifying large ontologies expressed in the tractable OWL 2 EL profile.

The Semantic Web–ISWC 2010, 2010

Ontology classification—the computation of subsumption hierarchies for classes and properties—is one of the most important tasks for OWL reasoners. Based on the algorithm by Shearer and Horrocks [9], we present a new classification procedure that addresses several open issues of the original algorithm, and that uses several novel optimisations in order to achieve superior performance. We also consider the classification of (object and data) properties. We show that algorithms commonly used to implement that task are incomplete ...