Abstract: A method and a system for discovering knowledge from text documents are disclosed, which involve extracting from text documents semi-structured meta-data, wherein the semi-structured meta-data includes a plurality of entities and a plurality of relations between the entities; identifying from the semi-structured meta-data a plurality of key entities and a corresponding plurality of key relations; deriving from a domain knowledge base a plurality of attributes relating to each of the plurality of entities relating to one of the plurality of key entities for forming a plurality of pairs of key entity and a plurality of attributes related thereto; formulating a plurality of patterns, each of the plurality of patterns relating to one of the plurality of pairs of key entity and a plurality of attributes related thereto; analyzing the plurality of patterns using an associative discoverer; and interpreting the output of the associative discoverer for discovering knowledge.

Abstract: A method and system for incorporating a user's preferences in an information clustering system having an information clustering engine for clustering information based on similarities, a user interface module for displaying the information groupings and obtaining user preferences, a personalization module for defining, labeling, modifying, storing and retrieving cluster structure, and a knowledge base where the user-defined cluster structure is stored. The user-configurable clustering system first organizes information into a list of automatic generated groupings or clusters. The list of groupings can then be modified by a user through an interactive process of personalization and re-clustering using a set of cluster manipulation operators. The personalized cluster indicates the user preference in organizing information and can be used to organize new information. The system can also be used to identify novel information that does not fit into the personalized cluster structure.

Abstract: A method and a system for discovering knowledge from text documents are disclosed, which involve extracting from text documents semi-structured meta-data, wherein the semi-structured meta-data includes a plurality of entities and a plurality of relations between the entities; identifying from the semi-structured meta-data a plurality of key entities and a corresponding plurality of key relations; deriving from a domain knowledge base a plurality of attributes relating to each of the plurality of entities relating to one of the plurality of key entities for forming a plurality of pairs of key entity and a plurality of attributes related thereto; formulating a plurality of patterns, each of the plurality of patterns relating to one of the plurality of pairs of key entity and a plurality of attributes related thereto; analyzing the plurality of patterns using an associative discoverer; and interpreting the output of the associative discoverer for discovering knowledge.

Abstract: A method and system for incorporating a users' preferences in an information clustering system, the user-configurable information clustering system comprising an information clustering engine for clustering of information based on similarities, a user interface module for displaying the information groupings and obtaining user preferences, a personalization module for defining, labeling, modifying, storing and retrieving cluster structure, and a knowledge base where the user-defined cluster structure is stored.