Critical Life Experiences that Mold a Person into a Global Scholar

Global Scholar Toni Fuss Kirkwood Tucker shares her experiences in Nazi Germany. This column contains an excerpt of Toni's presentation her award luncheon

Computing Compliant Anonymisations of Quantified ABoxes w.r.t. EL Policies

We adapt existing approaches for privacy-preserving publishing of linked data to a setting where the data are given as Description Logic (DL) ABoxes with possibly anonymised (formally: existentially quantified) individuals and the privacy policies are expressed using sets of concepts of the DL EL. We provide a chacterization of compliance of such ABoxes w.r.t. EL policies, and show how optimal compliant anonymisations of ABoxes that are non-compliant can be computed. This work extends previous work on privacy-preserving ontology publishing, in which a very restricted form of ABoxes, called instance stores, had been considered, but restricts the attention to compliance. The approach developed here can easily be adapted to the problem of computing optimal repairs of quantified ABoxes

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

Optimizing the computation of overriding

We introduce optimization techniques for reasoning in DLN---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size

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.

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