While writing up a call for papers on “integration” of ontologies, it occurred to me that ontologies are really low lying subjects for topic maps.
Any text corpus or database is going to require extraction of its content as a first step.
Your second step is going to be processing that extracted content to identify subjects.
Your third step is going to be creating topics and associations between topics, along with the properties of topics.
Your fourth step, depending on the purpose of your topic map, will be to create pointers back into the content for users (occurrences).
And finally, your fifth step, is going to be fashioning the interface your users will use for the topic map.
Compare those steps to topic mapping ontologies:
Your first step isn’t extraction of the data because while ontologies may exist in some format, they are free standing sets of subjects.
Your second step won’t be to identify subjects because the ontology already has subjects identified. (Yes, there are other subjects you could identify but this is the low-lying fruit version).
You avoid the third step because subjects in an ontology already have properties and relationships to other subjects.
You don’t need pointers because the entire ontology fits into your topic map, so no fourth step.
You have a familiar interface, the ontology itself, which leaves you with no fifth step.
Well, that’s a slight exaggeration. 😉
You do need the third step where subjects in the ontology get properties in addition to the ones they have in their respective ontologies. Those added properties enable the same subjects in different ontologies to merge. If their respective properties are also subjects in the ontology, that is they can have properties, you should be able to merge those properties as well.
I realize that the originators of some ontologies may disagree with the mappings but that really isn’t the appropriate question. The question is whether users find the mapping useful for some particular purpose. I am not sure what other test one would have?