Proposed changes to the classification wrapper to support new Drug Model

Problem:
In order to properly express what drugs are used for, we need to be able to express the 'Role' that they can play. Some NRCs will want to use these roles and others will not, as the additional attributes will cause modelling issues. Moving the additional roles into a new module would allow them to be included or filtered out as required.

These roles are additional definitions which, while true, are not part of the set of sufficiently defining conditions. The existing code for converting RF2 to OWL for classification - as it stands - puts all attributes into a single axiom, which would effectively including the roles in the sufficient set ie the fully defined definition, when we don't want them to. This is a problem because concepts that do have the sufficient set of defining attributes will no longer subsume under that concept if they don't also have the 'role' attribute.

We need to be able to support an aspect of EL called "Multiple Sufficient Sets" which is where you have your fully defined criteria, and then, separately, there are additional criteria which (if not fully defined) are not required to be present for a concept to be properly subsumed.

Proposed Solution:
The suggestion is to modify the classifier wrapper so that it collects relationships coming from different modules into separate axioms. This will give us the "Multiple Sufficient Sets" functionality that is required to prevent unwanted subsumption issues. This behaviour is actually how other OWL environments - like Protégé - work.

There is some overlap with the work being done currently by the Modelling AG which should also provide this functionality, but the proposed solution is an improvement to the current OWL transformation to bring it into line with accepted OWL practice. Also the Modelling AG work is an unknown time off implementation, and whereas this solution removes a blocker to Drugs work proposed for 2017.

Please send me your thoughts and comments as always - thanks,