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 and method can normalize documents or information objects into a graph of relationships between concepts (e.g., a knowledge base) and can normalize a search query into the same graph as a set of search criteria. The search can be executed using this graph and using indexes of concepts of the graph, and the results can be scored and presented to the user. User feedback (e.g., explicit and implicit feedback) can be collected to improve future searches.

Abstract: A targeted advertisement placement system allows an advertiser to place an ad in front of an audience based on specific interests of the users in that audience, and based on the advertiser's own specific interests in users who should view the ad. The ad placement system collects information about a population of users and a population of advertisers to create profiles that can include both explicit and implicit criteria defining the user's/advertiser's interest. The explicit criteria can be explicitly provided to the ad placement system, can be extracted from documents provided by the user and/or the advertiser, etc. The implicit criteria include information learned by the system about the interests of both advertisers and users based on preferences implicitly expressed when providing feedback on search results. Using these criteria collected from advertisers and users to build profiles, the system can compare the profiles for matches to determine the best audience before which to place an advertisement.

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. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match 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.

Abstract: A targeted advertisement placement system allows an advertiser to place an ad in front of an audience based on specific interests of the users in that audience, and based on the advertiser's own specific interests in users who should view the ad. The ad placement system collects information about a population of users and a population of advertisers to create profiles that can include both explicit and implicit criteria defining the user's/advertiser's interest. The explicit criteria can be explicitly provided to the ad placement system, can be extracted from documents provided by the user and/or the advertiser, etc. The implicit criteria include information learned by the system about the interests of both advertisers and users based on preferences implicitly expressed when providing feedback on search results. Using these criteria collected from advertisers and users to build profiles, the system can compare the profiles for matches to determine the best audience before which to place an advertisement.

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. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match 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.

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 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, including combined internal and profile weights, for producing personalized search results.

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. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match 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.

Abstract: A multi-way nested search utilizes one or more knowledge bases to allow a search engine to search through a database of target documents to find the best match for a given situation. In one node of the search, a user can input one or more search terms in an input string that can be searched against the database of target documents to select and score a resulting set of documents. In another node, a reference document can be input and search terms extracted for searching against the database of target documents to select and score a resulting set. In yet another node, a reverse search can be conducted in which the reference document is scored against the result set of target documents from the reference document search. These various search results and scores can be combined using combining functions to ultimately produce the most relevant set of target documents for the user. The user can control how much weigh is given to each search.

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.