Model-based approaches provide a semantically well justified way to revise ontologies. However, in general, model-based revision operators are limited due to lack of efficient algorithms and inexpressibility of the revision results. In this paper, we make both theoretical and practical contribution to efficient computation of model-based revisions in DL-Lite. Specifically, we show that maximal approximations of two well-known model-based revisions for DL-Lite_R can be computed using a syntactic algorithm. However, such a coincidence of model-based and syntactic approaches does not hold when role functionality axioms are allowed. As a result, we identify conditions that guarantee such a coincidence for DL-Lite_FR. Our result shows that both model-based and syntactic revisions can co-exist seamlessly and the advantages of both approaches can be taken in one revision operator. Based on our theoretical results, we develop a graph-based algorithm for the revision operat

RDF and Description Logics work in an open-world setting where absence of information is not information about absence. Nevertheless, Description Logic axioms can be interpreted in a closed-world setting and in this setting they can be used for both constraint checking and closed-world recognition against information sources. When the information sources are expressed in well-behaved RDF or RDFS (i.e., RDF graphs interpreted in the RDF or RDFS semantics) this constraint checking and closed-world recognition is simple to describe. Further this constraint checking can be implemented as SPARQL querying and thus effectively performed.

Rewriting is widely used to optimise owl:sameAs reasoning in materialisation based OWL 2 RL systems. We investigate issues related to both the correctness and efficiency of rewriting, and present an algorithm that guarantees correctness, improves efficiency, and can be effectively parallelised. Our evaluation shows that our approach can reduce reasoning times on practical data sets by orders of magnitude.

Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.

Uniform interpolation and the dual task of forgetting restrict the ontology to a specified subset of concept and role names. This makes them useful tools for ontology analysis, ontology evolution and information hiding. Most previous research focused on uniform interpolation of TBoxes. However, especially for applications in privacy and information hiding, it is essential that uniform interpolation methods can deal with ABoxes as well. We present the first method that can compute uniform interpolants of any ALC ontology with ABoxes. ABoxes bring their own challenges when computing uniform interpolants, possibly requiring disjunctive statements or nominals in the resulting ABox. Our method can compute representations of uniform interpolants in ALCO. An evaluation on realistic ontologies shows that these uniform interpolants can be practically computed, and can often even be presented in pure ALC.

We introduce Extended Property Paths (EPPs), a significant enhancement of SPARQL property paths. EPPs allow to capture in a succinct way a larger class of navigational queries than property paths. We present the syntax and formal semantics of EPPs and introduce two different evaluation strategies. The first is based on an algorithm implemented in a custom query processor. The second strategy leverages a translation algorithm of EPPs into SPARQL queries that can be executed on existing SPARQL processors. We compare the two evaluation strategies on real data to highlight their pros and cons.

Due to the openness and decentralization of the Web, mechanisms to represent and reason about the reliability of RDF data become essential. This paper embarks on a formal analysis of RDF data enriched with trust information by focusing on the characterization of its model-theoretic semantics and on the study of relevant reasoning problems. The impact of trust values on the computational complexity of well-known concepts related to the entailment of RDF graphs is studied. In particular, islands of tractability are identified for classes of acyclic and nearly-acyclic graphs. Moreover, an implementation of the framework and an experimental evaluation on real data are discussed.

In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel import-by-query algorithm that alternates steps of sub-query rewriting and of tailored querying the Linked Data cloud in order to import data as specific as possible for inferring or contradicting given target same-as facts. Experiments conducted on a real-world dataset have demonstrated the feasibility of this approach and its usefulness in practice for data linkage and disambiguation.

The Smart Grid allows users to monitor power usage through the use of Smart Meter technology. In principle, this information can be used to modify usage habits in a way that reduces consumer costs as well as greenhouse emissions. However, in an urban environment, many users are restricted by the same constaints: they work during the day, and they are home at night. This creates spikes in power cost at peak usage times, and it may also lead to increased emissions in scenarios where sustainable resources are limited. An individual user can avoid these spikes by using an electric car as a storage device; it can be charged at the cheapest times, and then discharged to the home at the most expensive times. While this idea is intuitively appealing, it turns out that the benefits vary greatly depending on the storage algorithm used. In this paper, we describe the Power Storage Simulator, a tool for experimenting with storage algorithms to improve the efficiency of vehicle to grid systems. We suggest that this tool is also useful for educating power consumers about load balancing on the Smart Grid through an engaging, visual simulation.

Systems biology, the study of the intricate, ramified, com-plex and interacting mechanisms underlying life, often proves too complex for unaided human understanding, even by groups of people working together. This difficulty is ex-acerbated by the high volume of publications in molecular biology. The Big C (‘C’ for Cyc) is a system designed to (semi-)automatically acquire, integrate, and use complex mechanism models, specifically related to cancer biology, via automated reading and a hyper-detailed refinement pro-cess resting on Cyc’s logical representations and powerful inference mechanisms. We aim to assist cancer research and treatment by achieving elements of biologist-level reason-ing, but with the scale and attention to detail that only com-puter implementations can provide.