Ontohub is a repository engine for managing distributed heterogeneous ontologies. The distributed nature enables communities to share and exchange their contributions easily. The heterogeneous nature makes it possible to integrate ontologies written in various ontology languages. Ontohub supports a wide range of formal logical and ontology languages, as well as various structuring and modularity constructs and inter-theory (concept) mappings, building on the OMG-standardized DOL language. Ontohub repositories are organised as Git repositories, thus inheriting all features of this popular version control system. Moreover, Ontohub is the first repository engine meeting a substantial amount of the requirements formulated in the context of the Open Ontology Repository (OOR) initiative, including an API for federation as well as support for logical inference and axiom selection.
Existing ontology resources on the web include search engines like Swoogle, Watson, and Sindice. They concentrate on (full-text and structured) search and querying. Ontology repositories also provide persistent storage and maintenance. TONES @cite_1 is a repository for OWL @cite_2 ontologies that provides some metrics, as well as an OWL sublanguage analysis. BioPortal @cite_13 is a repository that originates in the biomedical domain, but now has instances for various domains. Beyond browsing and searching, it provides means for commenting and aligning ontologies. Besides OWL, also related languages like OBO @cite_11 are supported. The NeOn Toolkit @cite_12 supports searching, selecting, comparing, transforming, aligning and integrating ontologies. It is based on the OWL API and is no longer actively maintained.
@cite_1 @cite_2 @cite_13 @cite_12 @cite_11
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Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http: bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protege frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping’ to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources. With OWL (Web Ontology Language) established as a standard for encoding ontologies on the Semantic Web, interest has begun to focus on the task of verbalising OWL code in controlled English (or other natural language). Current approaches to this task assume that axioms in OWL can be mapped to sentences in English. We examine three potential problems with this approach (concerning logical sophistication, information structure, and size), and show that although these could in theory lead to insuperable difficulties, in practice they seldom arise, because ontology developers use OWL in ways that favour a transparent mapping. This result is evidenced by an analysis of patterns from a corpus of over 600,000 axioms in about 200 ontologies. In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research efforts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of different intelligent environments. Abstract : Recently, there has been interest in marking up digital images with annotations describing the content of the images using Web-based ontologies encoded in the W3C's Web Ontology Language (OWL). The annotations are subsequently exploited to improve the user experience of large collections of images, whether by enhanced search or by a structured browsing experience. In the latter case, the complexity and unfamiliarity of logic-based ontology languages may do more to impede, than aid, the user. To alleviate this problem, the authors propose using automatic generation of natural language (NL) paraphrases of OWL statements to assist browsing image content. In this paper, they provide an overview of their NL generation approach and an empirical evaluation of the use of their paraphrases for image browsing.