Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSCs that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI , allowing it to generate much simpler and smaller concepts that are specific enough to answer a given query. With independence between computed MSCs, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries.
Based on the above lightweight DLs, efficient DL reasoners are developed, such as OWLIM @cite_9 , ELK reasoner @cite_43 , and Oracle's native inference engine for RDF data sets @cite_27 .
@cite_43 @cite_9 @cite_27
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Triple stores implementing the RL profile of OWL 2 are becoming increasingly popular. In contrast to unrestricted OWL 2, the RL profile is known to enjoy favourable computational properties for query answering, and state-of-the-art RL reasoners such as OWLim and Oracle's native inference engine of Oracle Spatial and Graph have proved extremely successful in industry-scale applications. The expressive restrictions imposed by OWL 2 RL may, however, be problematical for some applications. In this paper, we propose novel techniques that allow us (in many cases) to compute exact query answers using an off-the-shelf RL reasoner, even when the ontology is outside the RL profile. Furthermore, in the cases where exact query answers cannot be computed, we can still compute both lower and upper bounds on the exact answers. These bounds allow us to estimate the degree of incompleteness of the RL reasoner on the given query, and to optimise the computation of exact answers using a fully-fledged OWL 2 reasoner. A preliminary evaluation using the RDF Semantic Graph feature in Oracle Database has shown very promising results with respect to both scalability and tightness of the bounds. This inference engines are an integral part of semantic data stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle semantic data store. This inference engine computes production rule based entailment of one or more RDFS OWL encoded semantic data models. The inference engine capabilities include (i) inferencing based on semantics of RDFS OWL constructs and user-defined rules, (ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and (iii) validation of semantic data model based on RDFS OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach. An explosion in the use of RDF for representing information about resources has driven the requirements for Web-scale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform. The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph. Jena2, a second-generation RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the RDF recommendations, such as containers and reification. The Ontology API includes support for RDFS and OWL, including advanced OWL Full support. Jena includes the de facto reference RDF XML parser, and provides RDF XML output using the full range of the rich RDF XML grammar. N3 I O is supported. RDF graphs can be stored in-memory or in databases. Jena's query language, RDQL, and the Web API are both offered for the next round of standardization.