Efficient description logic reasoning in Prolog: The DLog system

Abstract

Traditional algorithms for description logic (DL) instance retrieval are inefficient for large amounts of underlying data. As description logic is becoming more and more popular in areas such as the Semantic Web and information integration, it is very important to have systems which can reason efficiently over large data sets.In this paper we present an approach to transform description logic axioms, formalised in the SHIQ DL language, into a Prolog program under the Unique Name Assumption. This transformation is performed with no knowledge about particular individuals: they are accessed dynamically during the normal Prolog execution of the generated program. This technique, together with the top-down Prolog execution, implies that only those pieces of data are accessed which are indeed important for answering the query. This 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.The transformation process consists of two steps: (1) the DL axioms are converted to first-order clauses of a restricted form, (2) a Prolog program is generated from these clauses.Step (2), which is the focus of the present paper, actually works on more general clauses than those obtainable by applying step (1) to a SHIQ knowledge base.We first present a base transformation, the output of which can either be executed using a simple interpreter, or further extended to executable Prolog code. We then discuss several optimisation techniques, applicable to the output of the base transformation. Some of these techniques are specific to our approach, while others are general enough to be interesting for description logic reasoner implementors not using Prolog.We give an overview of DLog, a DL reasoner in Prolog, which is an implementation of the techniques outlined above. We evaluate the performance of DLog and compare it to some widely used description logic reasoners, such as RacerPro, Pellet, and KAON2.

Two Phase Description Logic Reasoning for Efficient Information Retrieval

“…This is a resolution based reasoner, built on principles similar to KAON2. One difference is that instead of a datalog engine, we use the reasoning mechanism of the Prolog language [2] to perform the second phase [6]. Reasoning with function symbols using Prolog is possible, unlike the datalog engine, but for considerations about termination it is equally important to eliminate function symbols during the first phase.…”

On the Ostensibly Silent ‘W’ in OWL 2 RL

“…Secondly, we omit rules which mandate symmetric owl:sameAs inferences for all terms, viz: OWL 2 RL rule eq-ref (Table 3) 3 and pD* rules rdfp5a/rdfp5b [24, Table 6]. Such rules immediately add |T | statements to the graph and could be considered inflationary; they are, perhaps, better suited to backward-chaining support (in an approach such as [17]) than materialisation.…”

Efficient Two-Phase Data Reasoning for Description Logics

“…First, it can be an superposition with a clause of type (3) or (5). In the case of (3) the conclusion is decomposed (in terms of [6]) into clauses of type (3) and (10), while in the case of (5) we obtain a clause of type (10).…”

“…The transformation to Prolog uses the PTTP approach, a complete theorem prover technology for FOL [8]. The readers interested in the DLog system should consult [5]. The program is also available at http://dlog-reasoner.sourceforge.net.…”

“…Section 2 constitutes the core of this paper: it presents the first phase of DLog, i.e., how to transform the rules of a DL knowledge base into a function-free set of clauses which forms the basis of the subsequent efficient query driven data reasoning. Section 3 gives a brief overview to the DLog system which is described in detail in [5].…”