Overcoming the ontology enrichment bottleneck with Quick Term Templates
ABSTRACT When developing the Ontology of Biomedical Investigations (OBI), the process of adding classes with similar patterns of logical definition is time consuming, error prone, and requires an editor to have some expertise in OWL. Moreover, the process is poorly suited for a large number of domain experts who have limited experience with ontology development, and this can hinder contributions. We have developed a procedure to ease this task and allow such domain experts to add terms to the ontology in a way that both effectively includes complex logical definitions, yet requires minimal manual intervention by the OBI developers. The procedure is based on editing a Quick Term Template in a spreadsheet format that is subsequently converted into an OWL file. This procedure promises to be a robust and scalable approach for ontology enrichment as evidenced by encouraging results obtained when evaluated with an early version of the MappingMaster Protege plugin.
Full-textDOI: · Available from: Alan Ruttenberg, Aug 16, 2015
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ABSTRACT: Accurate representation of complex domains such as biology demands powerful and expressive ontology languages such as OWL. However, the complex nested class expressions required for modeling can be a hindrance to ontology authoring and adoption. These class expressions can appear opaque to domain experts, and even users proficient in OWL can benefit from some kind of syntactic sugar or "short-cut" strategy, especially when authoring large ontologies. One solution is to have domain experts fill in simple templates (for example, in Excel) and translate the results into more complex axioms, but this has the disadvantage of being disconnected from full ontology authoring and reasoning environment. We present here a method of specifying shortcut properties directly in OWL. These shortcut properties can be used in similar ways as object properties within the OWL environment, with the resulting simple axioms translated automatically to more complex axioms via macro expansion. We describe some example scenarios where this is of use in authoring existing bio-ontologies. One of the main implications of this work is a way to simplify the translation between OBO format and OWL, and the use of RDF triple-stores with complex OWL ontologies.Nature Precedings 12/2011; DOI:10.1038/npre.2011.5292.2
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ABSTRACT: Ontologies categorize entities, express relationships between them, and provide standardized definitions. Thus, they can be used to present and enforce the specific relationships between database components. The Immune Epitope Database (IEDB, http://www.iedb.org) utilizes the Ontology for Biomedical Investigations (OBI) and several additional ontologies to represent immune epitope mapping experiments. Here, we describe our experiences utilizing this representation in order to provide enhanced database search functionality. We applied a simple approach to incorporate the benefits of the information captured in a formal ontology directly into the user web interface, resulting in an improved user experience with minimal changes to the database itself. The integration is easy to maintain, provides standardized terms and definitions, and allows for subsumption queries. In addition to these immediate benefits, our long-term goal is to enable true semantic integration of data and knowledge in the biomedical domain. We describe our progress towards that goal and what we perceive as the main obstacles.Journal of Biomedical Semantics 04/2013; 4(1). DOI:10.1186/2041-1480-4-S1-S6 · 2.62 Impact Factor
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ABSTRACT: We present Protégé-OWL extensions designed to help scientists define domain-specific ontologies for describing observational data. The extensions pro-vide high-level forms that users can fill out from within Protégé to specify classes used to describe scientific measurements. As a user fills out a form, underlying OWL-DL axioms are automatically asserted, thus allowing users to specify rel-atively complex OWL-DL constraints without requiring an understanding of the technical details of OWL. Encoded in the constraints generated by the extension are a set of "best practices" for enabling improved data discovery and integration of observational data.