Exploiting conjunctive queries in description logic programs

2002

Abstract In this paper our goal is to bridge two popular and wellstudied knowledge representation formalisms: description logics (DLs), and declarative logic programs (DLPs). In recent years there has been tremendous development in both fields in terms of theoretical studies and implementations.

2016

Description logics (DLs) are a family of logic-based knowledge representation formalisms, which provide underlying semantics for modern ontology languages such as OWL 2. Reasoners for DLs are mostly specialised algorithms, which can answer questions such as whether an ontology as a whole is consistent, whether a certain concept in an ontology can be non-empty, or whether one concept subsumes another concept. Language extensions such as support for concrete domains (e.g. numbers) have been proposed in the past, but every extension typically requires the development of new reasoning algorithms. Some extensions have in fact never been implemented. This paper explores the use of CP technology for handling DL reasoning tasks. We show that CP modelling languages make it easy to extend DLs with new features, using a direct, high-level encoding into MiniZinc. Furthermore, our experiments show that modern CP solvers based on Lazy Clause Generation can be used as efficient DL reasoners. We pr...

2000

Abstract A serious shortcoming of many Description Logic based knowledge representation systems is the inadequacy of their query languages. In this paper we present a novel technique that can be used to provide an expressive query language for such systems. One of the main advantages of this approach is that, being based on a reduction to knowledge base satisfiability, it can easily be adapted to most existing (and future) Description Logic implementations.

Journal of automated …, 2007

We propose a new family of Description Logics (DLs), 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, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial time data complexity for query answering over DL knowledge bases. Notably our logics 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 Data Base Management Systems. Since it can be shown that even slight extensions to the logics of the DL-Lite family make query answering at least NLogSpace in data complexity, thus ruling out the possibility of using on-the-shelf relational technology for query processing, we can conclude that the logics of the DL-Lite family are the maximal DLs supporting efficient query answering over large amounts of instances.

2008

We consider the problems of conjunctive query answering and rewriting for information integration systems in which a Description Logic ontology is used to provide a global view of the data. We present a resolution-based query rewriting algorithm for DL-Lite+ ontologies, and use it to show that query answering in this setting is NLogSpace-complete with respect to data complexity. We also show that our algorithm produces an optimal rewriting when the input ontology is expressed in the language DL-Lite.

2004

As applications of description logics proliferate, efficient reasoning with large ABoxes (sets of individuals with descriptions) becomes ever more important. Motivated by the prospects of reusing optimization techniques from deductive databases, in this paper, we present a novel approach to checking consistency of ABoxes, instance checking and query answering, w.r.t. ontologies formulated using a slight restriction of the description logic SHIQ. Our approach proceeds in three steps: (i) the ontology is translated into firstorder clauses, (ii) TBox and RBox clauses are saturated using a resolution-based decision procedure, and (iii) the saturated set of clauses is translated into a disjunctive datalog program. Thus, query answering can be performed using the resulting program, while applying all existing optimization techniques, such as join-order optimizations or magic sets. Equally important, the resolution-based decision procedure we present is for unary coding of numbers worst-case optimal, i.e. it runs in EXPTIME.

Abstract. Description logic is expected to play a key role in ontology modeling for the Semantic Web. However, logic programming paradigm has been researched extensively in the past and has a large user community, so interoperability between these two formalisms is a desirable and an important issue. In this paper we extend our existing work on translation of description logics ontologies to logic programs.

… of the 10th International Workshop on …, 2003

Motivated by the possibilities of applying deductive database technology for efficient query answering in description logics, we present a translation operator µ that transforms non-recursive ALC ontologies into a disjunctive deductive database. Contrary to our previous work, in this paper we focus on handling negation, disjunction and existential quantifiers, which cannot be handled by deductive databases in a straightforward manner. We present a performance evaluation of our approach, confirming the intuition that techniques for optimizing query answering in disjunctive deductive databases may improve query answering in description logics.

Lecture Notes in Computer Science, 2006

Many modern applications of description logics (DLs) require answering queries over large data quantities, structured according to relatively simple ontologies. For such applications, we conjectured that reusing ideas of deductive databases might improve scalability of DL systems. Hence, in our previous work, we developed an algorithm for reducing a DL knowledge base to a disjunctive datalog program. To test our conjecture, we implemented our algorithm in a new DL reasoner KAON2, which we describe in this paper. Furthermore, we created a comprehensive test suite and used it to conduct a performance evaluation. Our results show that, on knowledge bases with large ABoxes but with simple TBoxes, our technique indeed shows good performance; in contrast, on knowledge bases with large and complex TBoxes, existing techniques still perform better. This allowed us to gain important insights into strengths and weaknesses of both approaches.

Journal of Automated Reasoning, 2007

As applications of description logics proliferate, efficient reasoning with knowledge bases containing many assertions becomes ever more important. For such cases, we developed a novel reasoning algorithm that reduces a \(\mathcal{SHIQ}\) knowledge base to a disjunctive datalog program while preserving the set of ground consequences. Queries can then be answered in the resulting program while reusing existing and practically proven optimization techniques of deductive databases, such as join-order optimizations or magic sets. Moreover, we use our algorithm to derive precise data complexity bounds: we show that \(\mathcal{SHIQ}\) is data complete for NP, and we identify an expressive fragment of \(\mathcal{SHIQ}\) with polynomial data complexity.