Abstract: A method of determining which users are experts and which tags are appropriate without some of the disadvantages of the prior art is described. The level of a user's expertise is determined based on previous tags, the categorization of one or more tags, and the rating of the tags previously left by the user. The appropriateness of a tag is based on previous tagging of information by the user, by the number of times a user has tagged information with the same categorization, and the rating of a user.

Abstract: Methods are disclosed in which participants in a communication session contribute tags—during the course of the communication session—such that the tags are visible to at least some of the participants as the communication session progresses. This creates a “live” collaborative tagging environment. Tags are contributed by some or all the participants on the communication session. The tags are displayed as they are generated. Moreover, one who declines to speak on the communication session can still actively tag. In this way, a tagger can participate and contribute in a collaborative live manner while the communication session is in progress. This approach departs from traditional “after the fact” or “note-taking” techniques in the prior art.

Abstract: A method to easily, quickly and visually identify experts, relevant tags, information associated with the experts, important topics, and emerging trends without some of the disadvantages of the prior art is disclosed. The present invention provides a novel user interface that connects general queries, users, tags, and information sources together.

Abstract: A method of determining which users are experts and which tags are appropriate without some of the disadvantages of the prior art is described. The level of a user's expertise is determined based on previous tags, the categorization of one or more tags, and the rating of the tags previously left by the user. The appropriateness of a tag is based on previous tagging of information by the user, by the number of times a user has tagged information with the same categorization, and the rating of a user.