Here’s a beautiful description of global sensemaking in one (long) sentence:
I’m thinking of means to learn about the existence of relevant new work (alert systems), find the texts and the passages we need (search engines), find work already found by colleagues (tagging and social networking systems), find articles similar to ones we know to be relevant (recommendation systems), find articles in our own language (machine translation), navigate to cited sources (reference linking), navigate to different versions of cited sources or other relevant destinations (multiple-resolution hyperlinks), convert a text to speech when we can’t read the screen (voice readers), paraphrase articles we don’t have time to read (text summarizers), digest larger volumes of literature than we could ever read (text mining), combine independent resources to create new synergies and utility (mash-ups), find information relevant to our questions even when we don’t know the relevant keywords (semantic web), distill uncopyrightable facts from natural-language texts and enter them into queryable OA databases (knowledge extraction), pose our search queries in our own words and sometimes even get back direct answers rather than mere pointers to literature that may contain answers (natural language search engines).
That’s from Peter Suber in the SPARC Open Access Newsletter, issue #123 of July 2, 2008. (SPARC is an acronym for Scholarly Publishing and Academic Resources Coalition.)
Suber talks about the “last-mile problem for knowledge” and its two stages:
- getting access to texts or data
- getting answers to questions
I think it’s a great essay with immediate relevance to what GSm is tackling.
[tip of the hat to Bora]