1. It feels a lot like literature-based-discovery. Is it ? if so , how is watson better than current literature based discovery methods that did create some scientific hypothesis AFAIK?
If not please share more details(if you can of course) about how it reaches hypothesis).
2. Does it work well in non biological literature ? Are there any examples ?
1. I am not super familiar with the space (I am not responsible for this specific product) so I may not answer your question exactly. The discovery product is an interactive tool more than a static tool generating hypothesis. It lets you search, navigate, visualize, query in natural language the literature and the extracted entities/relationships.
It uses a lot of pretty advanced tech to do that:
* State-of-the-art statistical annotators
* Some deep domain knowledge understanding of the entities themselves (e.g., how chemical entities are composed)
* Some nifty visualizations
* Many component from the Jeopardy stack (you can read the Jeopardy papers for more insights) - deep question parsing, candidate generation techniques, scorers, etc.
* Down the line it'll integrate more reasoning techniques along the lines of http://www.research.ibm.com/cognitive-computing/watson/watso...
2. Our first version was focused on life sciences and intelligence, we are expanding it to finance, law, and education.
A few questions:
1. It feels a lot like literature-based-discovery. Is it ? if so , how is watson better than current literature based discovery methods that did create some scientific hypothesis AFAIK?
If not please share more details(if you can of course) about how it reaches hypothesis).
2. Does it work well in non biological literature ? Are there any examples ?