Abstract: A service-oriented system architecture includes a computer-implemented search method and computer-implemented agent system for enabling efficient information searches on, for example, on XML databases, relational databases, and files located on intranets, the Internet, or other computer network systems. Referred to as the Knowledge Sifter architecture, the architecture may comprise, in one embodiment, a community of cooperating agents. The system architecture may be employed using a variety of methodologies, such as a case-based framework, collaborative filtering, or hybrid filtering. The case-based framework may be configured to recommend query specifications and refinements based on previously-stored user-query cases. Collaborative filtering involves the architecture recommending a set of unseen items that are preferred by other users to the active user. Hybrid filtering combines collaborative filtering and content-based filtering.

Abstract: Disclosed is a computer-implemented search method and computer-implemented agent system for efficient searching of information. Information may be located, for example, on XML databases, relational databases and files located on intranets and the Internet or other like computer network systems. Specifically, a service-oriented system architecture (also referred to as the Knowledge Sifter architecture), may be comprised of a community of cooperating agents. The system architecture may further be employed using a case-based framework configured to recommend query specifications and refinements based on previously-stored user-query cases. Other methodologies such as collaborative filtering, wherein the architecture can recommend a set of unseen items that are preferred by other users to the active user, can be used. Additionally, a hybrid filtering approach which combines both collaborative filtering and content-based filtering can be used effectively in the Knowledge Sifter architecture.

Abstract: Disclosed is a search mechanism comprising: accepting search intent information from a user having a search intent; creating a semantic taxonomy tree having term(s) representative of the search intent information; augmenting the term(s) with associated concepts derived from the term(s) using existing terminological data; associating a weight with at least one of the term(s); obtaining user preference intent; determining root term(s); transforming the semantic taxonomy tree to a Boolean search query; submitting the Boolean search query to searcher(s); receiving at least one search result(s); interpreting the search result(s); requesting page(s) specified the search result(s); receiving the page(s); generating ranked results; presenting the ranked results to the user; presenting the semantic taxonomy tree to the user; accepting user feedback from the user; and using the user feedback to update the user preference intent.