Predicting the understandability of OWL inferences

In this paper, we describe a method for predicting the understandability level of inferences with OWL. Specifically, we present a model for measuring the understandability of a multiple-step inference based on the measurement of the understandability of individual inference steps. We also present an evaluation study which confirms that our model works relatively well for two-step inferences with OWL. This model has been applied in our research on generating accessible explanations for an entailment of OWL ontologies, to determine the most understandable inference among alternatives, from which the final explanation is generated

Student’s Learning Profile as a Tool of Personal Learning Logistics

Increase of complexity and uncertainty as well as demand for personalization (including in education) urges universities to pay attention to educational subjectivity and its development; to transfer towards individual / collective-individual educational navigation and flexible systems of educational programs management (including formation of temporary learning groups, supply of required educational resources in due time, protocols of appraisal and mutual offset of educational results), what determines the relevance of the research. Usage of logistic approach enables to distinguish the pedagogical and management objectives of educational activity organization as well as to facilitate personalization of education. The article considers an educational profile as an instrument of personal educational logistics in digital educational environment, presents the preliminary terms “digital track”, “portfolio”, “profile”. The authors also dwell on the requirements to educational profiles development and scenarios of handling them in digital educational environment taking into account domestic and global experience of educational profiles’ implementation

Outcomes of surgery for high transsphincteric anal fistulas: prospective randomized trial

Background. Reliable data on the efficacy and safety of fistulectomy with primary sphincter repair for the treatment of high transsphincteric anal fistulas are deficient.The aim. To compare the efficacy and safety of fistulectomy with advancement muco-muscular flap (F) and fistulectomy with primary sphincter reconstruction (SR) for the treatment of high anorectal fistulas.Methods. A cohort of 92 consecutive patients with transsphincteric anal fistula involving 1/3 to 2/3 of the sphincteric complex were included in prospective randomized study. The primary endpoint was the recurrence rate. The duration of surgery, blood loss, pain intensity, postoperative complications, the duration of wound healing, incontinence, quality of life were registered.Results. Forty-six patients were randomized in each group. A statistically significant difference was obtained for operative time (Group “F” – 45 (20–160) min, Group “SR” – 33 (10–55) min). The blood loss was 3 (1–20) and 2 (1–10) ml in Groups “F” and “SR”, respectively (p = 0.482). The return to work in Groups “SR” and “F” occurred after 7 (2–14) and 8 (4–20) days, respectively (p = 0.005). The pain syndrome was significantly greater in Group “F” (p < 0.05) on days 1 and 7. Recurrence rate was in 23.9 % (11 cases) in Group “F” and in 6.5 % (3 cases) in Group “SR” (p = 0.042). Incontinence was in 7 (15.2 %) people in Group “F”, in 10 patients (21.7 %) – in Group “SR” (p = 0.591). There was no statistically significant difference in postoperative complications.Conclusion. Findings can expand the indications for the treatment of high transsphincteric anorectal fistulas involving from 1/3 to 2/3 of the sphincter complex without statistically significant risk for functional results

How Can Reasoner Performance of ABox Intensive Ontologies Be Predicted?

Reasoner performance prediction of ontologies in OWL 2 language has been studied so far from different dimensions. One key aspect of these studies has been the prediction of how much time a particular task for a given ontology will consume. Several approaches have adopted different machine learning techniques to predict time consumption of ontologies already. However, these studies focused on capturing general aspects of the ontologies (i.e., mainly the complexity of their TBoxes), while paying little attention to ABox intensive ontologies. To address this issue, in this paper, we propose to improve the representativeness of ontology metrics by developing new metrics which focus on the ABox features of ontologies. Our experiments show that the proposed metrics contribute to overall prediction accuracy for all ontologies in general without causing side-effects

Visual Ontology Cleaning: Cognitive Principles and Applicability

In this paper we connect two research areas, the Qualitative Spatial Reasoning and visual reasoning on ontologies. We discuss the logical limitations of the mereotopological approach to the visual ontology cleaning, from the point of view of its formal support. The analysis is based on three different spatial interpretations wich are based in turn on three different spatial interpretations of the concepts of an ontology.Ministerio de Educación y Ciencia TIN2004-0388

An introduction to description logics and query rewriting

This chapter gives an overview of the description logics underlying the OWL 2 Web Ontology Language and its three tractable profiles, OWL 2 RL, OWL 2 EL and OWL 2 QL. We consider the syntax and semantics of these description logics as well as main reasoning tasks and their computational complexity. We also discuss the semantical foundations for fist-order and datalog rewritings of conjunctive queries over knowledge bases given in the OWL2 profiles, and outline the architecture of the ontology-based data access system Ontop

General Terminology Induction in OWL

Abstract. Automated acquisition, or learning, of ontologies has attracted re-search attention because it can help ontology engineers build ontologies and give domain experts new insights into their data. However, existing approaches to on-tology learning are considerably limited, e.g. focus on learning descriptions for given classes, require intense supervision and human involvement, make assump-tions about data, do not fully respect background knowledge. We investigate the problem of general terminology induction, i.e. learning sets of general class in-clusions, GCIs, from data and background knowledge. We introduce measures that evaluate logical and statistical quality of a set of GCIs. We present methods to compute these measures and an anytime algorithm that induces sets of GCIs. Our experiments show that we can acquire interesting sets of GCIs and provide insights into the structure of the search space.

OntoFox: web-based support for ontology reuse

<p>Abstract</p> <p>Background</p> <p>Ontology development is a rapidly growing area of research, especially in the life sciences domain. To promote collaboration and interoperability between different projects, the OBO Foundry principles require that these ontologies be open and non-redundant, avoiding duplication of terms through the re-use of existing resources. As current options to do so present various difficulties, a new approach, MIREOT, allows specifying import of single terms. Initial implementations allow for controlled import of selected annotations and certain classes of related terms.</p> <p>Findings</p> <p>OntoFox <url>http://ontofox.hegroup.org/</url> is a web-based system that allows users to input terms, fetch selected properties, annotations, and certain classes of related terms from the source ontologies and save the results using the RDF/XML serialization of the Web Ontology Language (OWL). Compared to an initial implementation of MIREOT, OntoFox allows additional and more easily configurable options for selecting and rewriting annotation properties, and for inclusion of all or a computed subset of terms between low and top level terms. Additional methods for including related classes include a SPARQL-based ontology term retrieval algorithm that extracts terms related to a given set of signature terms and an option to extract the hierarchy rooted at a specified ontology term. OntoFox's output can be directly imported into a developer's ontology. OntoFox currently supports term retrieval from a selection of 15 ontologies accessible via SPARQL endpoints and allows users to extend this by specifying additional endpoints. An OntoFox application in the development of the Vaccine Ontology (VO) is demonstrated.</p> <p>Conclusions</p> <p>OntoFox provides a timely publicly available service, providing different options for users to collect terms from external ontologies, making them available for reuse by import into client OWL ontologies.</p