Current techniques for query answering over DL-Lite ontologies have severe limitations in practice, since they either produce complex queries that are inefficient during execution, or require expensive data pre-processing. In light of this, we present two complementary sets of results that aim at improving the overall peformance of query answering systems. We show how to create ABox repositories that are complete w.r.t. a significant portion of DL-Lite TBoxes, but where the data is not explicitly expanded. Second, we show how to characterize ABox completeness by means of dependencies, and how to use these and equivalence to optimize DL-Lite TBoxes. These results allow us to reduce the cost of query rewriting, often dramatically, and to generate highly efficient queries. We have implemented a novel system for query answering over DL-Lite ontologies that incorporates these techniques, and we present a series of data-intensive evaluations that show their effectiveness.

We are witnessing an explosion of available data from the Web, government authorities, scientific databases, sensors and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling the vast amounts of data for these applications. In this paper, we consider nonmonotonic reasoning, which has traditionally focused on rich knowledge structures. In particular, we consider defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge data sets. Our experimental results demonstrate that defeasible reasoning with billions of data is performant, and has the potential to scale to trillions of facts.

This paper presents a characterisation of and definitions for the phenomenon of masking in the context of justifications for entailments in ontologies. In essence masking is present within a justification, over a set of justifications, or over a complete ontology when the number of justifications for an entailment does not reflect the number of reasons for that entailment. Four types of masking are defined in this paper: Internal Masking, Cross Masking, External Masking and Shared Core Masking. The results of an empirical study are presented which shows that the phenomenon of masking is prevalent throughout ontologies with non-trivial entailments in the NCBO BioPortal corpus. Out of 72 ontologies, 53 exhibited some form of masking, with 9 ontologies exhibiting internal masking, 23 ontologies exhibiting external masking, and 53 ontologies exhibiting shared core masking.

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for DLs, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for (conjunctive) query answering over DL-Lite ontologies, by adopting abductive reasoning, that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning taskswe consider existence and recognition of an explanation, and relevance and necessity of a certain assertion for an explanation. We characterize the computational complexity of these problems for subset minimal and cardinality minimal explanations.

We define generalized ontology-based production systems (GOPSs), which formalize a very general and powerful combination of ontologies and production systems. We show that GOPSs capture and generalize many existing formal notions of production systems. We introduce a powerful verification query language for GOPSs, which is able to express the most relevant formal properties of production systems previously considered in the literature. We establish a general sufficient condition for the decidability of answering verification queries over GOPSs. Then, we define Lite-GOPS, a particular class of GOPSs based on the use of a light-weight ontology language (DL-Llite_A), a light-weight ontology query language (EQL-Lite(UCQ)), and a tractable semantics for updates over Description Logic ontologies. We show decidability of all the above verification tasks over Lite-GOPSs, and prove tractability of some of such tasks.

In ontology-based data access (OBDA), ontologies are used as an interface for querying instance data. Since in typical applications the size of the data is much larger than the size of the ontology and query, data complexity is the most important complexity measure. In this paper, we propose a new method for investigating data complexity in OBDA: instead of classifying whole logics according to their complexity, we aim at classifying each individual ontology within a given master language. Our results include a P/coNP-dichotomy theorem for ontologies of depth one in the description logic ALCFI, the equivalence of a P/coNP-dichotomy theorem for ALC/ALCI-ontologies of unrestricted depth to the famous dichotomy conjecture for CSPs by Feder and Vardi, and a non-P/coNP-dichotomy theorem for ALCF-ontologies.

We study (i) uniform interpolation for TBoxes that are formulated in the description logic EL and (ii) approximations of TBoxes formulated in more expressive languages. In both cases, we give model-theoretic characterizations based on simulations and cartesian products, and we develop algorithms that decide whether interpolants and approximations exist. We present a uniform approach to both problems, based on a novel amorphous automaton model called EL automata (EA). Using EAs, we also establish a simpler proof of the known result that conservative extensions of EL-TBoxes can be decided in ExpTime.

The key idea is that RDBMSs in practice appears to indicate only that answering data, 'stored in a standard relational database management real-world queries over real-world databases turns out to be system (RDBMS), can be queried through an OWL 2 QL tractable. As rewritings can turn a standard query to something ontology via a simple rewriting mechanism, i.e., by rewriting'out of this world,' a first rule of thumb could be as follows: the query into an SQL query that is then answered by the rewritten query should look similar to the original the RDBMS, without any changes to the data' (

The EL family of description logics (DLs) has been designed to provide a restricted syntax for commonly used DL constructors with the goal to guarantee polynomial complexity of reasoning. Yet, polynomial complexity does not always mean that the underlying reasoning procedure is efficient inpractice. In this paper we consider a simple DL ELO from the EL family that admits nominals, and argue that existing polynomial reasoning procedures for ELO can be impractical for many realistic ontologies. To solve the problem, we describe an optimization strategy in which the inference rules required for reasoning with nominals are avoided as much as possible. The optimized procedure is evaluated within the reasoner ELK and demonstrated to perform well in practice.

We consider the setting of ontological database access, where an A-box is given in form of a relational database D and where a Boolean conjunctive query q has to be evaluated against D modulo a T-box Σ formulated in DL-Lite or Linear Datalog+/-. It is well-known that (Σ, q) can be rewritten into an equivalent nonrecursive Datalog program P that can be directly evaluated over D. However, for Linear Datalog+/- or for DL-Lite versions that allow for role inclusion, the rewriting methods described so far result in a nonrecursive Datalog program P of size exponential in the joint size of Σ and q . This gives rise to the interesting question whether such a rewriting necessarily needs to be of exponential size. In this paper we show that it is actually possible to translate (Σ, q ) into a polynomially sized equivalent nonrecursive Datalog program P.