This Archimedia article has some tips about creating controlled vocabularies. There are two parts to the item, this is from the second:
“Try to think of what your image or asset is about, stay away from jargon words and acronyms. Examples:
Jargon words: ACID RAIN, DUE DILIGENCE, TEAM PLAYER, SPIN
Acronyms: FTP (File Transfer Protocol/Financial Transactions Plan/Functional Threshold Power), EMT (Emergency Medical Technician/Email Money Transfer) ,ATM (Automatic Teller Machine/Asynchronous Transfer Mode)” [Read More]
The first part also contains the familiar ‘pool’ example often used to indicate the potential for ambiguity which controlled vocabularies help to deal with in searches.
I do encounter a number of DAM users who think you can ignore controlled vocabularies and leave it all to the text search. This seems to be either because they don’t understand CV concepts or they have excessive faith in the ability of text search engines.
To illustrate how misplaced this is, a few years ago I wrote an article on controlled vocabularies where I used a similar example to the one above abut ‘pool’ being a reference to swimming pools, football pools (a form of soccer betting for our non-UK readers), or an alternative description for billiards. A few months later (and it still happens now) I noted lots of entries in our website logs from Google searches where people were looking for terms like ‘rules of pool’ or ‘system to win the pools’. The search engine Google use is probably somewhat more technically advanced than the commodity one your DAM system relies on.
It seems that although they can navigate driver-less cars around San Francisco, Google still can’t fully grasp the complexities of the English language and understand what people really mean based on the context of the conversation – and neither can DAM systems either. Controlled vocabularies augment DAM system search features by providing the necessary prompts from the end user to disambiguate between items in search results. Until we get some very much more sophisticated technology that can (correctly) work out all context-sensitive linguistic subtleties, they are essential if your media library contains more than a few hundred assets.
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