Assosiative Text... the Bernstein way
2006-5-27
Is this the lazy web at work? Hardly. But when I was reading Dr. Bernstein's (Eastgate/Tinderbox) blog lately, I felt a strong sense of "way cool, my wish is coming true!"
So what am I talking about? Mark Bernstein writes about something I deeply care for and have been experimenting with for a long time now: Automatically linking related/similar entries in a hypertext (eg a blog):
And looking at the CiteSeer citations for Mark's original paper I must say that I am only recreating a wheel that has been discussed quite thoroughly in the Hypertext research community. Well, I am absolutly no akademic, so most of that seems closed books for me. It's just not my language, although a lot of the ideas make very much sense once I dig into the academic style of these papers :)
Going back into my own past blogging about this topic I found the ideas quite scatteres and obfuscated in the usual blogging style. So I will try to collect and quote the most important ones I find below:
- I must say, those similar entries
"are really shaping up. Just go into the [permalink] version of a post you find interesting, and start browsing. I at least can see two things emerging very fast:
a) some topics are re-appearing regularly
b) I do repeat myself
This is almost like a search function, but somewhat better. To search you ned to know what you are looking for, esp. in terms of keywords. VectorSpace similar entries form the other side of that. 'Search/Query by Example' sort of. Now the ultimate would be a combination of both, keyword search and similar entries..." - similar entries really work
"a good point in demonstrating that keyword based search might not have worked here so well. I would have had the intention to find that posts, and the recollection that it actually was there in the first place. I would then had to enter some significant keywords and actively search... this way it just 'poped-up' in the right place for me, as I was exploring the same or similar topic again..." - Some thought on similarity
One thought that allways shows up with any kind of 'autogenerated categorisation' is this: 'Why are those two entries related, or why are they considered related?' Problem is, there are usually serveral kinds of relation entries can share. Same keywords, linking the same URL, by the same author etc.
In my experience thought rhat usually matters very little. Once one learns to take the results with some curiousness, and is expecting a surprise here and there, most relations auto-generated make perfect sense.
Given the realm I use this kind of FOA in, the 'surprises' are actually welcome. Boosting assosiative recall, finding buried knowledge, making new, unexpected connections between materials.
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