30. An apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge, comprising: means for, observing, between and among peers and experts who show high affinity with regard to any of users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, a processor for detecting whether the asset is or is not useful for a given user; the processor employing automatic techniques to extract patterns from at least detecting whether the asset is or is not useful for a given user; and the processor learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of: automatically determining each user's peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the terms as the terms become associated with more assets.
32. The apparatus of claim 30 , further comprising: the processor classifying usage of a given asset by a given user as successful or unsuccessful use based on a combination of factors comprising any of time spent on an asset, explicit user actions taken on the asset, the actions comprising any of print, save, email, and bookmark, mouse and keyboard behavior, length and distribution of stable pauses between activity, navigation to and from the asset, and browser window state, the state comprising any of foreground, background, minimized, maximized, and closed.