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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).
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    ABSTRACT: Developing ontologies can be expensive, time-consuming, as well as difficult to develop and maintain. This is especially true for more expressive and/or larger ontologies. Some ontologies are, however, relatively repetitive, reusing design patterns; building these with both generic and bespoke patterns should reduce duplication and increase regularity which in turn should impact on the cost of development. Here we report on the usage of patterns applied to two biomedical ontologies: firstly a novel ontology for karyotypes which has been built ground-up using a pattern based approach; and, secondly, our initial refactoring of the SIO ontology to make explicit use of patterns at development time. To enable this, we use the Tawny-OWL library which enables full-programmatic development of ontologies. We show how this approach can generate large numbers of classes from much simpler data structures which is highly beneficial within biomedical ontology engineering.
    12/2013;
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    ABSTRACT: Motivation: In this paper we demonstrate the usage of RIO; a framework for detecting syntactic regularities using clusteranalysis of the entities in the signature of an ontology. Quality assurance in ontologies is vital for their use inreal applications, as well as a complex and difficult task. It is also important to have such methods and toolswhen the ontology lacks documentation and the user cannot consult the ontology developers to understand itsconstruction. One aspect of quality assurance is checking how well an ontology complies with established'coding standards'; is the ontology regular in how descriptions of different types of entities are axiomatised?Is there a similar way to describe them and are there any corner cases that are not covered by a pattern?Detection of regularities and irregularities in axiom patterns should provide ontology authors and qualityinspectors with a level of abstraction such that compliance to coding standards can be automated. However,there is a lack of such reverse ontology engineering methods and tools. RESULTS: RIO framework allows regularities to be detected in an OWL ontology, i.e. repetitive structures in the axiomsof an ontology. We describe the use of standard machine learning approaches to make clusters of similarentities and generalise over their axioms to find regularities. This abstraction allows matches to, and deviationsfrom, an ontology's patterns to be shown. We demonstrate its usage with the inspection of three modules fromSNOMED-CT, a large medical terminology, that cover "Present" and "Absent" findings, as well as "Chronic"and "Acute" findings. The module sizes are 5 065, 20 688 and 19 812 asserted axioms. They are analysed interms of their types and number of regularities and irregularities in the asserted axioms of the ontology. Theanalysis showed that some modules of the terminology, which were expected to instantiate a pattern describedin the SNOMED-CT technical guide, were found to have a high number of regularity deviations. A subset ofthese were categorised as "design defects" by verifying them with past work on the quality assurance ofSNOMED-CT. These were mainly incomplete descriptions. In the worst case, the expected patterns describedin the technical guide were followed by only 5% of the axioms in the module. CONCLUSION: It is possible to automatically detect regularities and then inspect irregularities in an ontology. We argue thatRIO is a tool to find and report such matches and mismatches, for evaluations by the domain experts. We havedemonstrated that standard clustering techniques from machine learning can offer a tool in the drive for qualityassurance in ontologies.
    Journal of biomedical semantics. 12/2012; 3(1):8.

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