Abstract: A semantic vector is generated for a search term based upon a global frequency of other, closely related terms within a corpus that is used to compute the semantic vector relative to the search term. The semantic vector is used in connection with a textual search engine, responsive to a user query comprising a search term, to promote any of documents and sites within results returned to the query by the search engine that contain other, closely related terms that strongly correlate with the search term.

Abstract: A find engine provides a user with access to information via a find interface and a keep interface. The find interface accesses both a content search index and a personal search index; the keep interface accesses personal search index. The content search index lists document content, user generated content, and semantic vectors; the personal search index lists user generated content, user explicit gestures, and user implicit gestures. The user explicit gestures comprise keeps that are captured via the keep interface and that consist of any of one or more documents and sites from among the search results to which the user ascribes significance by affirmatively keeping the document or site. The keeps are preserved as one or more persistent search objects and the search results returned to the user are ranked and/or returned in response to the query at least in part in accordance with the keeps.

Abstract: An expert network in connection with keeping and finding information provides a textual search engine which, in response to a user query comprising a search term, uses a semantic vector to promote documents that contain other, closely related terms that strongly correlate with the search term. A social graph is generated for the user in which connections for the user comprise hyper-dimensional relationships based on semantic vectors that link the search term with a collection of the other, closely related terms. A personalized semantic vector for the user is applied to the social graph. The expert network applies the user's personalized semantic vector to locate experts in the user's social graph based on a user query topic. When a connection is considered an expert for the user, content that the connection kept is recommended to the user.