Answering queries using views in description logics

Journal of Web Semantics, 2012

2009

We present a family of expressive extensions of Datalog, called Datalog ± , as a new paradigm for query answering over ontologies. The Datalog ± family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. In particular, we show that query answering under so-called guarded Datalog ± is PTIME-complete in data complexity, and that query answering under so-called linear Datalog ± is in AC0 in data complexity. We also show how negative constraints and a general class of key constraints can be added to Datalog ± while keeping ontology querying tractable. We then show that linear Datalog ± , enriched with a special class of key constraints, generalizes the well-known DL-Lite family of tractable description logics. Furthermore, the Datalog ± family is of interest in its own right and can, moreover, be used in various contexts such as data integration and data exchange. This work is a short version of [8].

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.

Journal of Artificial Intelligence Research, 2008

Conjunctive queries play an important role as an expressive query language for Description Logics (DLs). Although modern DLs usually provide for transitive roles, conjunctive query answering over DL knowledge bases is only poorly understood if transitive roles are admitted in the query. In this paper, we consider unions of conjunctive queries over knowledge bases formulated in the prominent DL SHIQ and allow transitive roles in both the query and the knowledge base. We show decidability of query answering in this setting and establish two tight complexity bounds: regarding combined complexity, we prove that there is a deterministic algorithm for query answering that needs time single exponential in the size of the KB and double exponential in the size of the query, which is optimal. Regarding data complexity, we prove containment in co-NP.

2009

In the recent years, query answering over Description Logic (DL) knowledge bases has been receiving increasing attention, and various methods and techniques have been presented for this problem. In this paper, we consider knots, which are an instance of the mosaic technique from Modal Logic. When annotated with suitable query information, knots are a flexible tool for query answering that allows for solving the problem in a simple and intuitive way.

Artificial Intelligence, 2012

Ontology reasoning finds a relevant application in the so-called ontology-based data access, where a classical extensional database (EDB) is enhanced by an ontology, in the form of logical assertions, that generates new intensional knowledge which contributes to answering queries. In this setting, queries are therefore answered against a logical theory constituted by the EDB and the ontology; more specifically, query answering amounts to computing the answers to the query that are entailed by the EDB and the ontology. In this paper, we study novel relevant classes of ontological theories for which query answering is both decidable and of tractable data complexity, that is, the complexity with respect to the size of the data only. In particular, our new classes belong to the recently introduced family of Datalog-based languages, called Datalog ±. The basic Datalog ± rules are (functionfree) Horn rules extended with existential quantification in the head, known as tuplegenerating dependencies (TGDs). We propose the language of sticky sets of TGDs (or sticky Datalog ±), which are sets of TGDs with a restriction on multiple occurrences of variables in the rule-bodies. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that queries can be compiled into domain independent first-order (and thus translatable into SQL) queries over the given EDB. We also present several extensions of sticky sets of TGDs, and investigate the complexity of query answering under such classes. In summary, we obtain highly expressive and effective ontology languages that unify and generalize both classical database constraints, and important features of the most widespread tractable description logics; in particular, the DL-Lite family of description logics.

2007

Abstract Querying Description Logic knowledge bases has received great attention in the last years. In such a problem, the need of coping with incomplete information is the distinguishing feature with respect to querying databases.

Ontologies and rules play a central role in the development of the Semantic Web. Recent research in this context focuses especially on highly scalable formalisms for the Web of Data, which may highly benefit from exploiting database technologies. In this paper, as a first step towards closing the gap between the Semantic Web and databases, we introduce a family of expressive extensions of Datalog, called Datalog±, as a new paradigm for query answering over ontologies. The Datalog± family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. We show in particular that different versions of Datalog± generalize the tractable description logic ℇℒ and the DL-Lite family of tractable description logics, which are the most common tractable ontology languages in the context of the Semantic Web and databases. We also show how stratified negation can be added to Datalog± while keeping ontology querying tractable. Furthermore, the Datalog± family is of interest in its own right, and can, moreover, be used in various contexts such as data integration and data exchange. It paves the way for applying results from databases to the context of the Semantic Web.

1995

Two diierent aspects of data management are addressed by description logics (DL) and databases (DB): the semantic organization of data and powerful reasoning services (by DL) and their eecient management and access (by DB). It is recently emerging that experiences from both DL and DB should prootably cross-fertilize each other, and a great interest is rising about this topic. In the present paper our technique, that allows uniform access { by means of a DL-based query language { to information distributed over knowledge bases and databases, is brieey reviewed. Our extended paradigm integrates the separately existing retrieving functions of description logics management systems (DLMS) and of database management systems (DBMS) in order to allow, via a query language grounded on a DL-based schema knowledge , uniformly formulating and answering queries, so that uniform retrieval from mixed knowledge/data bases is possible. In particular, some new developments extending those presented i...