To situate topic maps in a traditional area of IR (information retrieval), try the “vocabulary problem.”
Furnas describes the “vocabulary problem” as follows:
Many functions of most large systems depend on users typing in the right words. New or intermittent users often use the wrong words and fail to get the actions or information they want. This is the vocabulary problem. It is a troublesome impediment in computer interactions both simple (file access and command entry) and complex (database query and natural language dialog).
In what follows we report evidence on the extent of the vocabulary problem, and propose both a diagnosis and a cure. The fundamental observation is that people use a surprisingly great variety of words to refer to the same thing. In fact, the data show that no single access word, however well chosen, can be expected to cover more than a small proportion of user’s attempts. Designers have almost always underestimated the problem and, by assigning far too few alternate entries to databases or services, created an unnecessary barrier to effective use. Simulations and direct experimental tests of several alternative solutions show that rich, probabilistically weighted indexes or alias lists can improve success rates by factors of three to five.
The Vocabulary Problem in Human-System Communication (1987)
Substitute topic maps for probabilistically weighted indexes or alias lists. (Techniques we are going to talk about in connection with topic maps authoring.)
Three to five times greater success is an incentive to use topic maps.
Marketing Department Summary
Customers can’t buy what they can’t find. Topic Maps help customers find purchases, increases sales. (Be sure to track pre and post topic maps sales results. So marketing can’t successfully claim the increases are due to their efforts.)