This paper describes a resolution based Description Logic reasoning system called DLog. DLog transforms Description Logic axioms into a Prolog program and uses the standard Prolog execution for efficiently answering instance retrieval queries. From the Description Logic point of view, DLog is an ABox reasoning engine for the full SHIQ language. The DLog approach makes it possible to store the individuals in a database instead of memory, which results in better scalability and helps using description logic ontologies directly on top of existing information sources. To appear in Theory and Practice of Logic Programming (TPLP).
Another approach of utilising Logic Programming in DL reasoning was proposed by the research group of the authors of the present paper. Earlier results of this work have been published in several conference papers. The first step of our research resulted in a resolution-based transformation of ABox reasoning problems to Prolog for the DL language @math and an @cite_11 . As the second step, we examined how ABox reasoning services can be provided with respect to a TBox: we extended our approach to allow ABox inference involving @math TBox axioms of a restricted form @cite_43 . In @cite_8 we presented a system doing almost full @math reasoning, which uses an interpreter based on PTTP techniques (see below).
@cite_43 @cite_8 @cite_11
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The goal of this paper is to present how the Prolog Technology Theorem Proving (PTTP) approach can be used for ABox-reasoning. This work presents an inference algorithm over the language ALC, and evaluates its performance highlighting the advantages and drawbacks of this method. We propose a new Description Logic, called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, conjunctive queries) over the set of instances maintained in secondary storage. We show that in DL-Lite the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is polynomial in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial data complexity for query answering over DL knowledge bases. A notable feature of our logic is to allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current DBMSs. 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. Many information-management tasks (including classification, retrieval, information extraction, and information integration) can be formalized as inference in an appropriate probabilistic first-order logic. However, most probabilistic first-order logics are not efficient enough for realistically-sized instances of these tasks. One key problem is that queries are typically answered by "grounding" the query---i.e., mapping it to a propositional representation, and then performing propositional inference---and with a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: in particular, every query @math can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well on an entity resolution task, a classification task, and a joint inference task; that the cost of inference is independent of database size; and that speedup in learning is possible by multi-threading.