The majority of DSOT’ers attended this event in July:
http://www.crl.edu/events/9347
Direct YouTube play:
(Ann Okerson made some real good remarks on librarian involvement @1:08-1:15 or so)
Think of text mining as “smart indexing” or “smart information retrieval”; data mining as “smart research” or “smart knowledge discovery”.
Gist: more automation, more discovery, for one goal: support better (less time, more results) research. (imagine the pain of reading 20 papers a day, as a human – i’d be happy to let my dogs do it if they can write me back a report with keywords and relations. fortunately, in addition to dogs, i have something called a computer, with programs)
Challenges: one platform; standardization of content format; open access vs. subscription content; licensing metadata; cross-disciplinary research (terms, context)
Resources to follow up with if you are real interested in the topic:
- http://www.publishingresearch.net/
- http://www.niso.org/news/pr/view?item_key=d2e5f409bc6af6b7f504a10edf0329203ffec6f9
- http://prospect.crossref.org/
- http://www.stm-assoc.org/text-and-data-mining-stm-statement-sample-licence/
- http://en.wikipedia.org/wiki/Biomedical_text_mining (*biomed and chemistry are the pioneers)
(btw, new knowledge of the day – Google Translate is statistics based instead of semantics – no wonder it’s so bad at Chinese and Japanese! Bu Hao!)