Topic maps are inherently designed for back-of-the-book indexes and have been extended to encompass other kinds such as glossaries, thesauri and cross references. But, they are too general to limit their use to their initial intended purposes. They can be used to encode arbitrarily complex knowledge structures and link them to information assets, which brings up the debate - which of the two, topic maps and RDF/OWL better for a given task?
There are many informative articles (
1,
2,
3) that discuss the differences between these two standards and how they can interoperate or even integrate. Most of the demonstrated applications of topic maps fall in the back-of-book indexing world for informational navigation (e.g.,
IRS Tax Map), and topic maps are a more natural choice for such applications. I wonder if one can build a good enough ontology in OWL to provide similar semantics for IRS Tax Map kind of applications - I haven't tried. Topics maps have no formal theory and don't guarantee computational completeness and decidability, which means one can shoot one's foot easily overusing topic maps for general knowledge representation.
I certainly wish there were only one set of standards, in stead of many, especially if the same purposes can be achieved with a minimal set of standards. Oh well, there is no ideal world.
