When Does Ontological Classification Work Well?
Domain to be Organized
* Small corpus, Formal categories, Stable entities, Restricted entities, Clear edges
Participants
* Expert catalogers, Authoritative source of judgment, Coordinated users, Expert users

Where it doesn’t?
Domain
* Large corpus, No formal categories, Unstable entities, Unrestricted entities, No clear edges
Participants
* Uncoordinated users, Amateur users, Naive catalogers, No Authority

The list of factors making ontology a bad fit is, also, an almost perfect description of the Web — largest corpus, most naive users, no global authority, and so on. The more you push in the direction of scale, spread, fluidity, flexibility, the harder it becomes to handle the expense of starting a cataloguing system and the hassle of maintaining it.

I think in many cases (like when trying to build a model of the web and it’s content’s) ontologies are best not pre-defined, more ideally the structures and hierarchies should emerge based on actual use/context. They are not static – they evolve and accumulate over time.

Since I began doing research in NLP, i started with non parametric methods, and also non static structures to modelize data. Indeed I did a toolkit mainly based in exploratory methods, I included all kinds of clustering algorithms, but specially focused on the fuzzy ones.