Obviously, extracting knowledge from the Web using a simplistic SAT approach is infeasible
However, I don't think it follows from this that extracting rich knowledge from the Web is infeasible It would require a complex system involving at least 1) An NLP engine that maps each sentence into a menu of probabilistically weighted logical interpretations of the sentence (including links into other sentences built using anaphor resolution heuristics). This involves a dozen conceptually distinct components and is not at all trivial to design, build or tune. 2) Use of probabilistic inference rules to create implication links between the different interpretations of the different sentences 3) Use of an optimization algorithm (which could be a clever use of SAT or SMT, or something else) to utilize the links formed in step 2, to select the right interpretation(s) for each sentence The job of the optimization algorithm is hard but not THAT hard because the choice of the interpretation of one sentence is only tightly linked to the choice of interpretation of a relatively small set of other sentences (ones that are closely related syntactically, semantically, or in terms of proximity in the same document, etc.). I don't know any way to tell how well this would work, except to try. My own approach, cast in these terms, would be to -- use virtual-world grounding to help with the probabilistic weighting in step 1 and the link building in step 2 -- use other heuristics besides SAT/SMT in step 3 ... but, using these techniques within NM/OpenCog is also a possibility down the road, I've been studying the possibility... -- Ben On Tue, Feb 26, 2008 at 6:56 AM, YKY (Yan King Yin) <[EMAIL PROTECTED]> wrote: > > > On 2/25/08, Ben Goertzel <[EMAIL PROTECTED]> wrote: > > Hi, > > > > There is no good overview of SMT so far as I know, just some technical > > papers... but SAT solvers are not that deep and are well reviewed in > > this book... > > > > http://www.sls-book.net/ > > > But that's *propositional* satisfiability, the results may not extend to > first-order SAT -- I've no idea. > > Secondly, the learning of an entire KB from text corpus is much, much harder > than SAT. Even the learning of a single hypothesis from examples with > background knowledge (ie the problem of inductive logic programming) is > harder than SAT. Now you're talking about inducing the entire KB, and > possibly involving "theory revision" -- this is VERY impractical. > > I guess I'd focus on learning simple rules, one at a time, from NL > instructions. IMO this is one of the most feasible ways of acquiring the > AGI KB. But it also involves the AGI itself in the acquisition process, not > just a passive collection of facts like MindPixel... > > YKY > > > ________________________________ > > agi | Archives | Modify Your Subscription -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] "If men cease to believe that they will one day become gods then they will surely become worms." -- Henry Miller ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=95818715-a78a9b Powered by Listbox: http://www.listbox.com