Re:Hutter Prize for Compressing Human Knowledge
by hcarvalhoalves on Sunday March 16, 2014 @02:06AM
(#7408086)
I had something else in mind, I think the parent thought that too.
Imagine, for instance, how many articles there are about how A has relation R with C, and B also (e.g., George Washington was a president of the USA, Abraham Lincoln also). For some definition of "understanding", you wouldn't need to encode data about the text of both articles A and B, you could use NLP to somehow store just the ontology. The problem would change from encoding text to encoding a graph. So it would be a lossy process (because you wouldn't be able to decompress into the original Wikipedia), but in my opinion seems more relevant to AI than bothering with the original corpus.
Imagine, for instance, how many articles there are about how A has relation R with C, and B also (e.g., George Washington was a president of the USA, Abraham Lincoln also). For some definition of "understanding", you wouldn't need to encode data about the text of both articles A and B, you could use NLP to somehow store just the ontology. The problem would change from encoding text to encoding a graph. So it would be a lossy process (because you wouldn't be able to decompress into the original Wikipedia), but in my opinion seems more relevant to AI than bothering with the original corpus.