The use of existing tools to implement rule-based mediation increases its usability for other researchers as well as decreases the development time. Therefore wherever possible, existing tools were used. Protege 3.4 was used to edit and view the ontologies in this project. The PSI-MIF syntactic ontology used for BioGRID and the UniProtKB syntactic ontology were generated using the XMLTab plugin for Protege 3.4. Once the syntactic ontologies are created, further editing can be performed at any time, as needed. This plugin has a number of advantages and disadvantages, but overall it was a good choice for the initial creation of the new syntactic ontologies.
XMLTab takes only a few seconds to creates syntactic ontologies. Additionally, if an XML file is provided instead of an XSD, then both classes and instances are generated in the syntactic ontology: the classes represent structural elements and attributes present in the XML file, while the actual data is created as instances within the ontology. The resulting OWL file is an exact duplicate of the XML structure, which is acceptable for the rule-based mediation methodology as semantic heterogeneity is resolved when the data is mapped from the syntactic ontology to the core ontology.
All mapping rules are available for viewing within
and were edited and run within the SWRLTab plugin for Protege 3.4. This
plugin requires the installation of the Jess Rule Engine for Java.
Firstly, all SWRL rules from the syntactic ontologies to the core ontologies were run (BP_*, PSIMIF_*, and UP_*). Secondly, the rules to further place the new instance data the telomere ontology (telomere_000*) were executed. Once complete, the telomere ontology can be queried with SQWRL and viewed. Finally, the queries and rules required for the use cases (telomere_MFO_* and telomere_SQWRL_*) are executed.
Protege 3.4 and the built-in Pellet 1.5.2 reasoner were used to reason over the entire set of integrated ontologies as well as just over the SBML model from the use case within MFO. When running on a dual-CPU laptop running Ubuntu Jaunty Jackalope with 4 Gigabytes of memory, consistency checks for the set of integrated ontologies take approximately 10-15 seconds, while computing inferred types requires approximately 9-12 minutes. Reasoning over just MFO with the annotated model from the use case takes less than 10 seconds for both consistency checks and the computation of inferred types. Future work will include decreasing reasoning times via more efficient reasoners and ontologies, as well as through the use of faster computers. Once the data from the use cases are exported back into MFO, the MFO toolset was used to convert the newly-annotated model from OWL to SBML.