Computational protocol: The iOSC3 System: Using Ontologies and SWRL Rules for Intelligent Supervision and Care of Patients with Acute Cardiac Disorders

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Protocol publication

[…] This activity involved the representation of the ontology in a formal language. The ontology was formalized in OWL DL, a description logics-based sublanguage of the Ontology Web Language (OWL) []. It was chosen because it is highly expressive and it still retains computational completeness and decidability. In addition, several well-known reasoning systems are available for OWL DL, such as Pellet. The ontology was built using the Stanford University ontology editor Protégé (version 3.4.7) []. The inference rules were written in the Semantic Web Rule Language (SWRL) [], which is the rule representation language recommended by the Semantic Web community and allows to express rules on the basis of ontology concepts. The rules were written using the SWRL Editor (see ), a development environment for working with SWRL rules in Protégé-OWL. When editing rules in this environment, users can directly refer to OWL classes, properties, and individuals within an OWL ontology. They also have direct access to a full set of built-ins described in the SWRL built-in specification and to all of the XML Schema data types []. The rules were stored as OWL individuals in the C3O ontology. shows an example of rule used by the expert system written both in natural language and in the Semantic Web Rule Language.The resulting ontology has been called Cardiac Critical Care Ontology (C3O). It contains 40 well-defined terms (classes) frequently used by experts in the area of CICUs organized as a taxonomy, 1 object property, 5 datatype properties, and a set of inference rules that guide the decision making process. The C3O ontology in OWL format is publicly available at http://tinyurl.com/cyeqq6x. [...] According to the knowledge-reuse principles proposed by the OBO Foundry [], at this phase, we incorporated to the C3O ontology knowledge already provided by other ontologies. We checked if the identified concepts were already contained in other existing biomedical ontologies. Carrying out this process manually is a hard and time-consuming task; so, we used a biomedical ontology selection tool (the BIOSS system (http://bioss.ontologyselection.com/) [, ]). We observed that most of the concepts were distributed across different ontologies. Also, some concepts had not been previously defined. As an example, the MeSH ontology (version 2009_02_13) contains the concepts “dobutamine” and “infusion pump,” but it does not contain the concept “Mean Arterial Pressure,” which is contained in the NCI Thesaurus ontology (version 2008_05D). We referenced the concepts contained in other ontologies and created the concepts that had not been previously defined. […]

Pipeline specifications

Software tools Protégé, BiOSS
Databases OBO Foundry
Application Ontology generation
Organisms Homo sapiens
Diseases Heart Diseases