I mentioned the webinar: Driving Knowledge-Worker Performance with Precision Search Results a few days ago in Findability As Value Proposition.
There was one nugget (among many) in the webinar before I lose sight of how important it is to topic maps and semantic technologies in general.
Dan Taylor (Earley and Associates) was presenting a maturation diagram for knowledge technologies.
See the presentation for the details but what struck me was than on the left side (starting point) there were documents. On the right side (the goal) were answers.
Think about that for a moment.
When you search in Google or any other search engine, what do you get back? Pointers to documents, presentations, videos, etc.
What task remains? Digging out answers from those documents, presentations, videos.
A mature knowledge technology goes beyond what an average user is searching for (the Google model) and returns information based on a specific user for a particular domain, that is, an answer.
For the average user there may be no better option than to drop them off in the neighborhood of a correct answer. Or what may be a correct answer to the average user. No guarantees that you will find it.
The examples in the webinar are in specific domains where user queries can be modeled accurately enough to formulate answers (not documents) to answer queries.
Reminds me of TaxMap. You?
If you want to do a side by side comparison, try USC: Title 26 – Internal Revenue Code. From the Legal Information Institute (Cornell)
Don’t get me wrong, the Cornell materials are great but they reflect the U.S. Code, nothing more or less. That is to say the text you find there isn’t engineered to provide answers. 😉
I will update this point with the webinar address as soon as it appears.