  In mathematics information is a number that measures uncertainty (entropy)
based on a probabilistic distribution, often of an obscure origin. In real life
language information is a datum, a statement, more precisely, a formula. But
such a formula should be justified by a proof. I try to formalize this
perception of information. The measure of informativeness of a proof is based
on the set of proofs related to the formulas under consideration. This set of
possible proofs (`a knowledge base') defines a probabilistic measure, and
entropic weight is defined using this measure. The paper is mainly conceptual,
it is not clear where and how this approach can be applied.
