Abstract: Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction/matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database parsing an input query from a user conducting the search of the database into sub-strings, and matching the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts. Furthermore, the system can learn user preferences to construct one or more profiles for producing personalized search results.

Abstract: A concept synonym matching engine identifies and extracts specific information referenced in a selection of text and matches the information to a set of defined concepts. The engine is able to perform this identification and matching in the presence of errors (e.g., misspellings, etc.) or variations in the description of those concepts (e.g., use of different terms to define the same idea). The engine performs these functions by tokenizing an input string of text and matching these tokens to tokens of a pattern that represents a concept to be matched. The engine matches the pattern to a sub-string of text, scores the match, and uses the score to determine whether the concept is present in the input string or to select the optimal match.