We propose a novel, type-elimination-based method for reasoning in the description logic SHIQbs including DL-safe rules. To this end, we first establish a knowledge compilation method converting the terminological part of an ALCIb knowledge base into an ordered binary decision diagram (OBDD) which represents a canonical model. This OBDD can in turn be transformed into disjunctive Datalog and merged with the assertional part of the knowledge base in order to perform combined reasoning. In order to leverage our technique for full SHIQbs, we provide a stepwise reduction from SHIQbs to ALCIb that preserves satisfiability and entailment of positive and negative ground facts. The proposed technique is shown to be worst case optimal w.r.t. combined and data complexity and easily admits extensions with ground conjunctive queries.

Social media analytics has been attracting considerable attention in both research and industry due to the increasing popularity of social media usage. As a subset, social media monitoring describes the process of continuous monitoring of a subject matter in social media. From our point of view, the key requirements for such systems are i) high throughput and real-time processing of incoming data, ii) a user-friendly way to define complex situations of interests that make use of formalized background knowledge and iii) capabilities to perform actions based on gained insights instead of a pure monitoring system. In this paper, we propose a system for (pro) active, real-time social media monitoring. Firstly, we describe the conceptual architecture of our system and necessary pre-processing steps. Secondly, we introduce our concept of semantic requests that is capable to extend event pattern definitions with background knowledge. Finally, we show the usefulness of this system in two different domains: Real-time political opinion tracking and proactive establishment of relationships with consumers in order to perform a new form of real-time marketing. The main advantage of our approach is a simplified, expressive way to formulate event patterns in social media applications.

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.

A counterexample, inconsistency tolerant query answering methods, known now as the Yale Shooting Scenario, showed and mechanisms supporting update operations that early attempts to make the idea of commonsense expressed over the ontology.

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.

Datalog ∃ is the extension of Datalog, allowing existentially quantified variables in rule heads. This language is highly expressive and enables easy and powerful knowledge-modeling, but the presence of existentially quantified variables makes reasoning over Datalog^E undecidable, in the general case. The results in this paper enable powerful, yet decidable and efficient reasoning (query answering) on top of Datalog ∃ programs. On the theoretical side, we define the class of parsimonious Datalog ∃ programs, and show that it allows of decidable and efficiently-computable reasoning. Unfortunately, we can demonstrate that recognizing parsimony is undecidable. However, we single out Shy, an easily recognizable fragment of parsimonious programs, that significantly extends both Datalog and Linear-Datalog ∃ , while preserving the same (data and combined) complexity of query answering over Datalog, although the addition of existential quantifiers. On the practical side, we implement a bottom-up evaluation strategy for Shy programs inside the DLV system, enhancing the computation by a number of optimization techniques to result in DLV ∃ — a powerful system for answering conjunctive queries over Shy programs, which is profitably applicable to ontology-based query answering. Moreover, we carry out an experimental analysis, comparing DLV ∃ against a number of state-of-the-art systems for ontology-based query answering. The results confirm the effectiveness of DLV ∃ , which outperforms all other systems in the benchmark domain.

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.

While query answering in the presence of description logic (DL) ontologies is a well-studied problem, questions of static analysis such as query containment and query optimization have received less attention. In this paper, we study a rather general version of query containment that, unlike the classical version, cannot be reduced to query answering. First, we allow a restriction to be placed on the vocabulary used in the instance data, which can result in shorter equivalent queries; and second, we allow each query its own ontology rather than assuming a single ontology for both queries, which is crucial in applications to versioning and modularity. We also study global minimization of queries in the presence of DL ontologies, which is more subtle than for classical databases as minimal queries need not be isomorphic.

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.