On 3/3/2011 7:12 PM, Dávid Tóth wrote: This would be very useful for me. I'm thinking about attack this problem by discovering 'low hanging fruits'. To some extent you can assume that :X :wikiLink :Y -> :X skos:related :Y but the nature and strength of links is hard to estimate. I've developed a good metric for approximating the importance of topic :X, but I've yet to get a handle on relationship strength. To take an example, there's a link from :Metallica to :Yale_University because :Metallica :Sued :Yale_University That's not a very strong connection. Now, if Wikipedia mentioned the "Dead at Cornell" recording which was made when Jerry Garcia had just gotten hooked on opium and the band was playing at it's best, we might say :Grateful_Dead :PlayedAt :Cornell_University maybe you think that's a stronger connection than the above, maybe you don't. Then again, :Rod_Serling :TaughtAt :Ithaca_College is one of the stronger links involving :Ithaca_College in my opinion. There are two angles I see for extracting better relationships from Wikipedia and these are (i) databases such as Freebase and DBPedia, in particular, these have certain relationships already semantized and other information that can be used to infer about possible relationships. For instance, :Brown_Bear :Sued :Pelican doesn't make any sense and should be rejected. (ii) analysis of the text around a link. You could certainly see certain language patterns that are frequently used, for instance "A is a B", "C married D", "E was born at F" you could either find some of these by hand or you could write something that uses machine learning techniques to discover these. Information from type (i) could be useful here. For instance, we could find a bunch of relationships that exist in Freebase and use these as positive training examples. The trouble I see here is the creation of a good set of negative training examples, which has a few aspects: one is that examples that should be positive will slip into a negative sample, attempts to automatically exclude positives will probably also exclude 'near miss' negatives that would be especially important to include training set, and generally, the number of negatives would be 1000 or more times prevalent than positives, which gives most ML methods Bayesian priors that destroy recall. Another issue is that you'll see the patterns "E was born at F" "[[E]] was born at F" "E was born at [[F]]" "[[E]] was born at [[F]]" all occur (sometimes they make the text describing the subject a link, sometimes they don't.) Getting good recall then means solving the named entity extraction problem as well, however, making this part of a 'whole system' might create the kind of feedback control loop that's necessary for high-performing A.I. The best attack on this, I think, is to pick one particular relationship that you want to extract, particularly one that has a bit of a 'closed world' aspect in that you can presume that that property ought to exist for all members of a type. For instance, we can say that "any person was born at some location" but even there you can get into trouble quick, if you look at :Joan_of_arc, you see that wikipedia says that she was "A peasant <http://en.wikipedia.org/wiki/Peasant> girl born in eastern France" you note that "A peasant girl" == :Joan_of_arc and that a more specific birthplace can be found in the infobox.