We study stream reasoning in DatalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to forward-propagating DatalogMTL programs, in which propagation of derived information towards past time points is precluded. Memory consumption in our generic algorithm depends both on the properties of the rule set and the input data stream; in particular, it depends on the distances between timestamps occurring in data. This may be undesirable in certain practical scenarios since these distances can be very small, in which case the algorithm may require large amounts of memory. To address this issue, we propose a second algorithm, where the size of the required memory becomes independent on the timestamps in the data at the expense of disallowing punctual intervals in the rule set. We have implemented our approach as an extension of the DatalogMTL reasoner MeTeoR and tested it experimentally. The obtained results support the feasibility of our approach in practice.

Faceted search is a prominent search paradigm that became the standard in many Web applications and has also been recently proposed as a suitable paradigm for exploring and querying RDF graphs. One of the main challenges that hampers usability of faceted search systems especially in the RDF context is information overload, that is, when the size of faceted interfaces becomes comparable to the size of the data over which the search is performed. In this demo we present (an extension of) our faceted search system SemFacet and focus on features that address the information overload: ranking, aggregation, and reachability. The demo attendees will be able to try our system on an RDF graph that models online shopping over a catalogs with up to millions of products.

Answering aggregate queries is a key requirement of emerging applications of Semantic Technologies, such as data warehousing, business intelligence, and sensor networks. To fulfil the requirements of such applications, the standardization of SPARQL 1.1 led to the introduction of a wide range of constructs that enable value computation, aggregation, and query nesting. In this article, we provide an in-depth formal analysis of the semantics and expressive power of these new constructs as defined in the SPARQL 1.1 specification, and hence lay the necessary foundations for the development of robust, scalable, and extensible query engines supporting complex numerical and analytics tasks.

Answering aggregate queries is a key requirement of emerging applications of Semantic Technologies, such as data warehousing, business intelligence and sensor networks. In order to fulfill the requirements of such applications, the standardisation of SPARQL 1.1 led to the introduction of a wide range of constructs that enable value computation, aggregation, and query nesting. In this paper we provide an in-depth formal analysis of the semantics and expressive power of these new constructs as defined in the SPARQL 1.1 specification, and hence lay the necessary foundations for the development of robust, scalable and extensible query engines supporting complex numerical and analytics tasks.

An increasing number of applications rely on RDF, OWL 2, and SPARQL for storing and querying data. SPARQL, however, is not targeted towards end-users, and suitable query interfaces are needed. Faceted search is a prominent approach for end-user data access, and several RDF-based faceted search systems have been developed. There is, however, a lack of rigorous theoretical underpinning for faceted search in the context of RDF and OWL 2. In this paper, we provide such solid foundations. We formalise faceted interfaces for this context, identify a fragment of first-order logic capturing the underlying queries, and study the complexity of answering such queries for RDF and OWL 2 profiles. We then study interface generation and update, and devise efficiently implementable algorithms. Finally, we have implemented and tested our faceted search algorithms for scalability, with encouraging results.

Triple stores implementing the RL profile of OWL 2 are becoming increasingly popular. In contrast to unrestricted OWL 2, the RL profile is known to enjoy favourable computational properties for query answering, and state-of-the-art RL reasoners such as OWLim and Oracle's native inference engine of Oracle Spatial and Graph have proved extremely successful in industry-scale applications. The expressive restrictions imposed by OWL 2 RL may, however, be problematical for some applications. In this paper, we propose novel techniques that allow us (in many cases) to compute exact query answers using an off-the-shelf RL reasoner, even when the ontology is outside the RL profile. Furthermore, in the cases where exact query answers cannot be computed, we can still compute both lower and upper bounds on the exact answers. These bounds allow us to estimate the degree of incompleteness of the RL reasoner on the given query, and to optimise the computation of exact answers using a fully-fledged OWL 2 reasoner. A preliminary evaluation using the RDF Semantic Graph feature in Oracle Database has shown very promising results with respect to both scalability and tightness of the bounds.

Applications of semantic technologies often require the representation of and reasoning with structured objects - that is, objects composed of parts connected in complex ways. Although OWL is a general and powerful language, its class descriptions and axioms cannot be used to describe arbitrarily connected structures. An OWL representation of structured objects can thus be underconstrained, which reduces the inferences that can be drawn and causes performance problems in reasoning. To address these problems, we extend OWL with description graphs, which allow for the description of structured objects in a simple and precise way. To represent conditional aspects of the domain, we also allow for SWRL-like rules over description graphs. Based on an observation about the nature of structured objects, we ensure decidability of our formalism. We also present a hypertableau-based decision procedure, which we implemented in the HermiT reasoner. To evaluate its performance, we have extracted description graphs from the GALEN and FMA ontologies, classified them successfully, and even detected a modeling error in GALEN.

The ability to extract meaningful fragments from an ontology is key for ontology re-use. We propose a definition of a module that guarantees to completely capture the meaning of a given set of terms, i.e., to include all axioms relevant to the meaning of these terms, and study the problem of extracting minimal modules. We show that the problem of determining whether a subset of an ontology is a module for a given vocabulary is undecidable even for rather restricted sub-languages of OWL DL. Hence we propose two "approximations", i.e., alternative definitions of modules for a vocabulary that still provide the above guarantee, but that are possibly too strict, and that may thus result in larger modules: the first approximation is semantic and can be computed using existing DL reasoners; the second is syntactic, and can be computed in polynomial time. Finally, we report on an empirical evaluation of our syntactic approximation which demonstrates that the modules we extract are surprisingly small.

In the semantic Web architecture, Web ontology languages arebuilt on top of RDF(S). However, serious difficulties have arisen when trying to layer expressive ontology languages, like OWL, on top of RDF-Schema. Although these problems can be avoided, OWL (andthe whole semantic Web architecture) becomes much more complex than it should be. In this paper, a possible simplification of thesemantic Web architecture is suggested, which has several import antadvantages with respect to the layering currently accepted by the W3C Ontology Working Group.