Abstract: A query processing system (QPS) is described herein for interpreting a user's input query against a structured knowledge base, to provide an output result. The output result may include one or more query suggestions, each providing a recommendation as to how a user may refine his or her query. In addition, or alternatively, the output result may specify one or more entity items which satisfy the user's query. In interpreting the user's query, the QPS may rely on a collection of rule modules which identify and process different types of constraints that may be expressed in the input query, including numeric constraint, nested constraints, comparison-based constraints, and so on.

Abstract: In a method and system for matching learning objects with a user profile, a top-level concept structure, including a keyword hierarchy for each of one or more top-level concepts, can be used to determine the complexity of a learning object. The learning object can include one or more learning object keywords related to one or more top-level concepts contained in the top-level concept structure. The learning object keywords can be assigned a complexity score based upon their location within the keyword hierarchy, and the complexity scores for all of the learning object keywords relating to a top-level concept can be aggregated to generate a learning object top-level concept complexity score. The learning object top-level concept complexity score can be compared with a user's experience rating for that top-level concept and be used to select an appropriate learning object.

Abstract: In a method and system for matching learning objects with a user profile, a top-level concept structure, including a keyword hierarchy for each of one or more top-level concepts, can be used to determine the complexity of a learning object. The learning object can include one or more learning object keywords related to one or more top-level concepts contained in the top-level concept structure. The learning object keywords can be assigned a complexity score based upon their location within the keyword hierarchy, and the complexity scores for all of the learning object keywords relating to a top-level concept can be aggregated to generate a learning object top-level concept complexity score. The learning object top-level concept complexity score can be compared with a user's experience rating for that top-level concept and be used to select an appropriate learning object.