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.
On the other hand, for efficient ABox reasoning and instance checking, various optimization techniques have been developed, including lazy unfolding, absorption, heuristic guided search, exploration of Horn clauses of DLs @cite_28 @cite_0 @cite_45 , model merging @cite_40 and extended pseudo model merging technique @cite_32 @cite_11 .
@cite_28 @cite_32 @cite_0 @cite_40 @cite_45 @cite_11
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A fundamental problem in Description Logics (DLs) is satisfiability, the problem of checking if a given DL terminology T remains sufficiently unconstrained to enable at least one instance of a given DL concept C to exist. It has been known for some time that lazy unfolding is an important optimization technique in model building algorithms for satisfiability [2]. It is also imperative for large terminologies to be manipulated by an absorption generation process to maximize the benefits of lazy unfolding in such algorithms, thereby reducing the combinatorial effects of disjunction in underlying chase procedures [5]. In this paper, we propose a generalization of the absorption theory and algorithms developed by Horrocks and Tobies [6, 7]. The generalization, called binary absorption, makes it possible for lazy unfolding to be used for parts of terminologies not handled by current absorption algorithms and theory. The basic idea of binary absorption is to avoid the need to internalize (at least some of the) terminological axioms of the form 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. Automated verification of multi-threaded programs is difficult. Direct treatment of all possible thread interleavings by reasoning about the program globally is a prohibitively expensive task, even for small programs. Rely-guarantee reasoning is a promising technique to address this challenge by reducing the verification problem to reasoning about each thread individually with the help of assertions about other threads. In this paper, we propose a proof rule that uses rely-guarantee reasoning for compositional verification of termination properties. The crux of our proof rule lies in its compositionality wrt. the thread structure of the program and wrt. the applied termination arguments --- transition invariants. We present a method for automating the proof rule using an abstraction refinement procedure that is based on solving recursion-free Horn clauses. To deal with termination, we extend an existing Horn-clause solver with the capability to handle well-foundedness constraints. Finally, we present an experimental evaluation of our algorithm on a set of micro-benchmarks.