Tassos Venetis

Query Extensions and Incremental Query Rewriting for OWL 2 QL Ontologies

Query rewriting over lightweight ontologies, like DL-Lite ontologies, is a prominent approach for ontology-based data access. It is often the case in realistic scenarios that users ask an initial query which they later refine, e.g., by extending it with new constraints making their initial request more precise. So far, all DL-Lite systems would need to process the new query from scratch. In this paper, we study the problem of computing the rewriting of an extended query by ‘extending’ a previously computed rewriting of the initial query and avoiding recomputation. Interestingly, our approach also implies a novel algorithm for computing the rewriting of a fixed query. More precisely, the query can be ‘decomposed’ into its atoms and then each atom can be processed incrementally. We present detailed algorithms, several optimisations for improving the performance of our query rewriting algorithm, and finally, an experimental evaluation.

A Fuzzy Extension to the OWL 2 RL Ontology Language

Fuzzy extensions to Description Logics (DLs) have gained considerable attention the last decade. So far most works on fuzzy DLs have focused on either very expressive languages, like fuzzy OWL and OWL 2, or on highly inexpressive ones, like fuzzy OWL 2 QL and fuzzy OWL 2 EL. To the best of our knowledge a fuzzy extension to the language OWL 2 RL has not been thoroughly studied so far. This language is very relevant since it combines both adequate expressive power as well as efficient reasoning algorithms which can be realised using rule-based (Datalog) technologies. In contrast to previous fuzzy extensions, a fuzzy extension of OWL 2 RL is not a straightforward task for the following reason. The main motivation of OWL 2 RL is that its axioms can be equivalently represented as Datalog rules. Hence, to achieve our goal we need to investigate which OWL 2 RL axioms when interpreted under the fuzzy setting can be transformed to equivalent fuzzy Datalog rules. We show that this is not, in general, possible for all axioms but we show that this “issue” can to a large extent be alleviated. Moreover, we have performed an experimental evaluation with many well-known ontologies which showed that such axioms are not used so often in practice.

Data Integration in the Human Brain Project

The Medical Informatics Platform of the Human Brain Project has the challenging task of organizing and presenting to its users a variety of data originating from different hospitals and hospital systems in a unified way, while protecting patients privacy as imposed by national legislation and institutional ethics. In this paper we view these challenges under the scope of data integration and analyze preliminary steps taken towards realizing the Medical Informatics Platform.

Rewriting Minimisations for Efficient Ontology-Based Query Answering

Computing a (Union of Conjunctive Queries - UCQ) rewriting R for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. However, R can be large and complex in structure hence additional techniques, like query subsumption and data constraints, need to be employed in order to minimise Rew and lead to an efficient evaluation. Although sound in theory, how to efficiently and effectively implement many of these techniques in practice could be challenging. For example, many systems do not implement query subsumption. In the current paper we present several practical techniques for UCQ rewriting minimisation. First, we present an optimised algorithm for eliminating redundant (w.r.t. subsumption) queries as well as a novel framework for rewriting minimisation using data constraints. Second, we show how these techniques can also be used to speed up the computation of R in the first place. Third, we integrated all our techniques in our query rewriting system IQAROS and conducted an extensive experimental evaluation using many artificial as well as challenging real-world ontologies obtaining encouraging results as, in the vast majority of cases, our system is more efficient compared to the two most popular state-of-the-art systems.

Enabling Data Integration Using MIPMap

In previous work we have analysed the infrastructure of the Human Brain Project Medical Informatics Platform focusing on the challenges related to dataintegration based on a visual data exchange tool, called MIPMap. In this paper we present new MIPMap features that enhance the integration process and data access.

Rewriting Minimizations for Efficient Query Answering over Ontologies

Computing a (Union of Conjunctive Queries — UCQ) rewriting ℛ for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. How...

A Framework for Clustering and Classification of Big Data Using Spark

Nowadays, massive data sets are generated in many modern applications ranging from economics to bioinformatics, and from social networks to scientific databases. Typically, such data need to be processed by machine learning algorithms, which entails high processing cost and usually requires the execution of iterative algorithms. Spark has been recently proposed as a framework that supports iterative algorithms over massive data efficiently. In this paper, we design a framework for clustering and classification of big data suitable for Spark. Our framework supports different restrictions on the data exchange model that are applicable in different settings. We integrate k-means and ID3 algorithms in our framework, leading to interesting variants of our algorithms that apply to the different restrictions on the data exchange model. We implemented our algorithms over the open-source computing framework Spark and evaluated our approach in a cluster of 37-nodes, thus demonstrating the scalability of our techniques. Our experimental results show that we outperform the algorithm provided by Spark for k-means up to 31 %, while the centralized k-means is at least one order of magnitude worse.

Towards the Identification of Disease Signatures

The identification of biological signatures of diseases will enable the development of new biologically grounded classifications of brain diseases, leading to a new systematic understanding of their causes, and new diagnostic tools. In this paper we present the challenges and steps taken towards the identification of disease signatures, through the Medical Informatics Platform of the Human Brain Project, that will expedite diagnosis and lead to more accurate prognosis and objective diagnosis.