ABSTRACT. In this paper we present TROvE(Tool for Rapid OWL Reasoner Evaluation), a tool aimed to offer to a non-expert user the possibility to evaluate OWL reasoners on several reasoning tasks by means of a simple “push-button” solution.

ABSTRACT. Engines for query answering over ontological knowledge bases are becoming increasingly popular and important in areas like the Semantic Web or information integration. They are mostly designed to answer queries over ontologies expressed using various Description Logics and in the presence of large amounts of instance data. This computational task, known as ontology-based query answering (OQA), is an important component in the more general area of ontology-based data access. Unfortunately, it has been acknowledged that judging the performance of current OQA reasoners and their underlying algorithms is difficult due to the lack of publicly available benchmarks that consist of large amounts of real-life instance data. In this paper we describe how benchmarks for OQA systems can be created from the publicly available OpenStreetMap (OSM) geospatial data. To this end, we first develop a formalization of OSM and present a rule-based language to specify the rules to extract instance data from OSM data. The declarative nature of the approach allows various variants of a benchmark to be created via small modifications to the rules of the specification. We describe a highly flexible engine to create a benchmark from a given OSM map and a given set of rules and present some evaluation results.

ABSTRACT. In ontology-based data access (OBDA), the aim is to provide a high-level conceptual view over potentially very large (relational) data sources by means of a mediating ontology. The ontology is connected to the data sources through a declarative specification given in terms of mappings that relate each (class and property) symbol in the ontology to an (SQL) view over the data. Although prototype OBDA systems providing the ability to answer SPARQL queries over the ontology are available, a significant challenge remains: performance. To properly evaluate OBDA systems, benchmarks tailored towards the requirements in this setting are needed. OWL benchmarks, which have been developed to test the performance of generic SPARQL query engines, however, fail at 1) exhibiting a complex real-world ontology, 2) providing challenging real world queries, 3) providing large amounts of data, and the possibility to test a system over data of increasing size, and 4) capturing important OBDA-specific measures related to the rewriting-based query answering approach in OBDA. In this work, we propose a novel benchmark for OBDA systems based on a real world use-case adopted in the EU project Optique. We validate our benchmark on the system Ontop, showing that it is more adequate than previous benchmarks not tailored for OBDA.

ABSTRACT. This paper evaluates the performances of the OWL 2 reasoners HermiT, FaCT++ and TReasoner in the context of an ontological decision support system in designing entity-relationship diagrams during software development. First, I described a developed ontology which is the knowledge base of the developed application for designing databases. In the first set of experiments I compared how the classification and realization time of the DBOM ontology varied when increasing the ABox with ERD elements individuals. In the second set of experiments the consistency checking time of the DBOM ontology was estimated by increasing the ABox with ERD elements individuals.

ABSTRACT. We introduce the tool Just for computing justifications for general concept inclusions w.r.t. ontologies formulated in the description logic EL extended with role inclusions. The computation of justifications in Just is based on saturating the input axioms under all possible inferences w.r.t. a consequence-based calculus. We give an overview of the architecture of the tool and we conclude with an experimental evaluation of its performance when applied on several practical ontologies.

ABSTRACT. Applications for mobile devices could often show a more intelligent behaviour by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL~2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL~2 DL and OWL~2 EL reasoners on Android smartphones and tablets.

ABSTRACT. We describe the Data Mining OPtimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. DMOP contains in-depth descriptions of DM tasks, data, algorithms, hypotheses, and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. The choices made led to v5.4 of the DMOP ontology. We report some evaluations on processing DMOP with a standard reasoner by considering different DMOP features.

ABSTRACT. Genetic data can be used to optimize drug treatment based on the genetic profiles of individual patients, thereby reducing adverse drug events and improving the efficacy of pharmacotherapy. The Genomic Clinical Decision Support (Genomic CDS) ontology utilizes Web Ontology Language 2 (OWL 2) reasoning for this task. The ontology serves a clear-cut medical use case that requires challenging OWL 2 DL reasoning. We present an update of the Genomic CDS ontology which covers a significantly larger number of clinical decision support rules and where inconsistencies present in previous versions of the ontology have been removed.

ABSTRACT. This paper presents a challenging family history knowledge-base (FHKB) authored in OWL 2 DL. Originally, the FHKB was designed to act as a tool for education, especially about OWL 2’s features and the use of automated reasoners. As a result, the FHKB has been constructed to maximise use of inference. For individuals representing people, only genealogical assertions on parentage and sparse assertions of siblinghood are given explicitly. All other genealogical inferences are driven by a rich property hierarchy, property characteristics and subproperty chains. A rich collection of entailments are generated, but reasoners struggle to handle a version with all of Robert’s known relatives.

ABSTRACT. This paper presents an updated version of Mini-ME, a mobile reasoner for the Semantic Web of Things. Building upon previous stronger elements, i.e. computational efficiency and support for non-standard inference services, novel features have been added. Particularly, the Concept Covering reasoning task for request answering via service/resource composition has been included among allowed inferences, Protégé plugins have been released and the support for OWLlink protocol is now available. As a proof of concept, two use cases are presented, both in the mobile and ubiquitous computing field: a wireless semantic sensor network and a mobile semantic augmented reality scenario.

ABSTRACT. Reasoning in complex DLs is well known to be expensive. However, in numerous application scenarios, the ontology in use is either never modified at all (e.g., in query answering), or the amount of updates is negligible in comparison with the whole ontology (e.g., minor manual edits, addition of a few individuals). In order to efficiently support these scenarios, FaCT++ implements the following two techniques: persistent and incremental reasoning. In persistent reasoning mode, after classification, the reasoner saves its internal state, including computed information (e.g., concept taxonomy) on a persistent medium; the next use of the ontology will not require classification to be performed from scratch, but just reading an input file. In incremental reasoning mode, the reasoner is notified of a change and identifies a (usually small) portion of its internal state that is affected by the change. This is the only part that requires recomputation. This approach can lead to greater overall efficiency, when compared with having to reload and reclassify the whole ontology.