36. A method for learning user preferences in a search of knowledge base to construct one or more profiles for producing personalized search results, the method comprising: receiving feedback from the user regarding quality of search results presented to the user in a search of a knowledge base that is a semantic network of relationships among concepts and that provides an index of a plurality of documents, the feedback representing how well the search results match an input query provided by the user, the search results including one or more of the documents indexed by the knowledge base; constructing the one or more profiles for the user based on the feedback received, where each of the search results that receive feedback values are used to construct a model that includes profile weights computed from the feedback values; modifying internal weights used for scoring search criteria applied in producing the search results presented to the user, the modifications made based on the profile weights in the constructed model; generating implicit search criteria for the user based on the one or more profiles by: defining simple profiled score criteria that are based on a single attribute or attribute path, the simple profiled score criteria instantiated as simple profiled score criteria values; wherein a partial score of the simple profiled score criteria value is computed using a similarity measure between a first vector including active profile weights and a second vector corresponding to values referenced by a target concept, where dimensions of the first and second vectors are defined by the values associated with the attribute path specified by the simple profiled score criteria, and wherein length of dimensions of the first vector are defined by the profile weights; and applying the implicit search criteria and modified weights during a subsequent search of the knowledge base conducted by the user producing a subsequent set of search results that are personalized to the user.
36. A method for learning user preferences in a search of knowledge base to construct one or more profiles for producing personalized search results, the method comprising: receiving feedback from the user regarding quality of search results presented to the user in a search of a knowledge base that is a semantic network of relationships among concepts and that provides an index of a plurality of documents, the feedback representing how well the search results match an input query provided by the user, the search results including one or more of the documents indexed by the knowledge base; constructing the one or more profiles for the user based on the feedback received, where each of the search results that receive feedback values are used to construct a model that includes profile weights computed from the feedback values; modifying internal weights used for scoring search criteria applied in producing the search results presented to the user, the modifications made based on the profile weights in the constructed model; generating implicit search criteria for the user based on the one or more profiles by: defining simple profiled score criteria that are based on a single attribute or attribute path, the simple profiled score criteria instantiated as simple profiled score criteria values; wherein a partial score of the simple profiled score criteria value is computed using a similarity measure between a first vector including active profile weights and a second vector corresponding to values referenced by a target concept, where dimensions of the first and second vectors are defined by the values associated with the attribute path specified by the simple profiled score criteria, and wherein length of dimensions of the first vector are defined by the profile weights; and applying the implicit search criteria and modified weights during a subsequent search of the knowledge base conducted by the user producing a subsequent set of search results that are personalized to the user. 41. The method of claim 36 , further comprising: learning user profiles and search profiles; applying these to the search to modify ranking of the search results.