Towards practical query answering for Horn-SHIQ

Annals of Mathematics and Artificial Intelligence, 2008

We present cq-programs, which enhance nonmonotonic description logics (dl-) programs by conjunctive queries (CQ) and union of conjunctive queries (UCQ) over Description Logics knowledge bases, as well as disjunctive rules. dlprograms had been proposed as a powerful formalism for integrating nonmonotonic logic programming and DL-engines on a clear semantic basis. The new cq-programs have two advantages. First, they offer increased expressivity by allowing general (U)CQs in the body. And second, this combination of rules and ontologies gives rise to strategies for optimizing calls to the DL-reasoner, by exploiting (U)CQ facilities of the DL-reasoner. To this end, we discuss some equivalences which can be exploited for program rewriting and present respective algorithms. Experimental results for a cq-program prototype show that this can lead to significant performance improvements.

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.

2010

Abstract. We consider the complexity of answering conjunctive queries in the description logic S, ie, in ALC extended with transitive roles. While a co-NEXPTIME lower bound was recently established in [4], the best known upper bound was 2-EXPTIME. In this paper, we concentrate on the case where only a single transitive role (and no other role) is present and establish a tight co-NEXPTIME upper bound.

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.

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.

1999

Abstract Answering queries using views amounts to computing the answer to a query having information only on the extension of a set of views. This problem is relevant in several fields, such as information integration, data warehousing, query optimization, etc. In this paper we address the problem of query answering using views for nonrecursive datalog queries embedded in a Description Logics (equipped with ز-ary relations) knowledge base. We present the following results.

2008

Abstract. We consider the problems of conjunctive query answering and rewriting under Description Logic constraints. We present a query rewriting algorithm for ELHI knowledge bases, and use it to show that query answering in this setting is PTime-complete wrt data complexity. We show that our algorithm is worst-case optimal for languages with data complexity of query answering ranging from LogSpace to PTimecomplete.

2012

We present a novel rewriting technique for conjunctive query answering over OWL 2 QL ontologies. In general, the obtained rewritings are not necessarily correct and can be of exponential size in the length of the query. We argue, however, that in most, if not all, practical cases the rewritings are correct and of polynomial size. Moreover, we prove some sufficient conditions, imposed on queries and ontologies, that guarantee correctness and succinctness. We also support our claim by experimental results. m j=1 D j over propositional variables p 1 , . . . , p n as an OWL 2 QL TBox,

2018

Most query rewriting systems focus on answering Conjunctive Queries that have only positive atoms because the use of negation can easily make the entailment problem undecidable. However, restricting the way we use negation on Conjunctive Queries can make the entailment problem decidable and can also ensure the existence of algorithms based on Query Rewriting that find their answers. In this paper, we propose a definition of Horn Conjunctive Queries to denote queries that have one negated atom. We also present an algorithm based on query rewriting that finds the answers of a Union of Horn Conjunctive Queries with respect to a set of Existential Rules. Additionally, we conduct some experiments to compare the performance with other state of the art systems that are able to answer conjunctive queries with negation using tableaux algorithms. The experimental data confirms that our system performs better than the other systems we considered when dealing with big ontologies.