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.
Recently, techniques for partitioning or modularizing ABoxes into logically-independent fragments have been developed @cite_23 @cite_44 . These techniques partition ABoxes into logically-independent modules, such that each will preserve complete information of a given set of individuals, and thus can be reasoned independently w.r.t. the TBox and be able to take advantage of existing parallel-processing techniques.
@cite_44 @cite_23
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The extraction of logically-independent fragments out of an ontology ABox can be useful for solving the tractability problem of querying ontologies with large ABoxes. In this paper, we propose a formal definition of an ABox module, such that it guarantees complete preservation of facts about a given set of individuals, and thus can be reasoned independently w.r.t. the ontology TBox. With ABox modules of this type, isolated or distributed (parallel) ABox reasoning becomes feasible, and more efficient data retrieval from ontology ABoxes can be attained. To compute such an ABox module, we present a theoretical approach and also an approximation for @math ontologies. Evaluation of the module approximation on different types of ontologies shows that, on average, extracted ABox modules are significantly smaller than the entire ABox, and the time for ontology reasoning based on ABox modules can be improved significantly. This paper investigates optimization techniques and data structures exploiting the use of so-called pseudo models. These techniques are applied to speed up TBox and ABox reasoning for the description logics ALCNHR+ and ALC(D). The advances are demonstrated by an empirical analysis using the description logic system RACE that implements TBox and ABox reasoning for ALCNHR+. Recently, several approaches have been proposed on combining description logic (DL) reasoning with database techniques. In this paper we report on the LAS (Large Abox Store) system extending the DL reasoner Racer with a database used to store and query Tbox and Abox information. LAS stores for given knowledge bases their taxonomy and their complete Abox in its database. The Aboxes may contain role assertions. LAS can answer Tbox and Abox queries by combining SQL queries with DL reasoning. The architecture of LAS is based on merging techniques for so-called individual pseudo models. Methods for mechanically synthesizing concurrent programs for temporal logic specifications have been proposed by Emerson and Clarke and by Manna and Wolper. An important advantage of these synthesis methods is that they obviate the need to manually compose a program and manually construct a proof of its correctness. A serious drawback of these methods in practice, however, is that they produce concurrent programs for models of computation that are often unrealistic, involving highly centralized system architecture (Manna and Wolper), processes with global information about the system state (Emerson and Clarke), or reactive modules that can read all of their inputs in one atomic step (Anuchitanukul and Manna, and Pnueli and Rosner). Even simple synchronization protocols based on atomic read write primitives such as Peterson's solution to the mutual exclusion problem have remained outside the scope of practical mechanical synthesis methods. In this paper, we show how to mechanically synthesize in more realistic computational models solutions to synchronization problems. We illustrate the method by synthesizing Peterson's solution to the mutual exclusion problem.