Oracle International Corporation

Orack Corporation

James Conklin

Kelly Wical

Mohammad Faisal

Joshua Powers

A terminological system automatically generates sub-categories from categories of a knowledge base. The knowledge base includes a plurality of hierarchically arranged categories, as well as terms associated with the categories. A subset of the categories of the knowledge base are designated &ldquo;dimensional categories.&rdquo; The system also stores a corpus of documents, including themes and corresponding theme weights for each document. A target category is selected to generate sub-categories. A set of themes from the corpus of documents are selected for each term. Dimensional category vectors, one for each term, are generated by associating the set of themes for a term to a dimensional category in the knowledge base. The dimensional category vectors for each term are analyzed to determine if one or more clusters of terminological groups exist to generate new sub-categories. A content processing system, which generates themes and theme weights, is also disclosed.

Automated category discovery for a terminological knowledge base

A cluster processing system determines at least one focal node on a hierarchically arranged tree structure of nodes based on attributes of a data set. The data set comprises a plurality of data set attributes with associated weight values. The cluster processing system selects a set of nodes from the tree structure with tree structure attributes that correspond with the data set attributes, and then assigns quantitative values to nodes in the set of nodes from the weight values in the data set. At least one cluster of nodes are selected, based on proximity in the tree structure, and at least one focal node on the tree structure for the cluster of nodes is selected. The focal node comprises an attribute most representative of the data set attributes. A terminological system learns the meaning of terms (attributes of a data set) by identifying categories (nodes) from a knowledge catalog (trees structure).

Methods and apparatus for determining focal points of clusters in a tree structure

A search and retrieval system pre-processes an input query to map a contextual semantic interpretation, expressed by the user of the input query, to a boolean logic interpretation for processing in the search and retrieval system. A knowledge base comprises a plurality of categories, such that subsets of the categories are designated to one of a plurality of groups. A lexicon stores a plurality of terms including definitional characteristics for the terms. To pre-process the query, the search and retrieval system receives an input query comprising a plurality of terms, and processes the terms by referencing the lexicon to identify value terms that comprise a content carrying capacity. The knowledge base is referenced to identify a group for each value term. A processed input query is generated by inserting an AND logical connector between two value terms if the two respective value terms are in different groups and by inserting an OR logical connector between two value terms if the two respective value terms are in the same group. The lexicon is also used to identify phrases as well as connective terms for conversion to a boolean operator.

Free format query processing in an information search and retrieval system

An information retrieval system processes user input queries, and identifies query feedback, including ranking the query feedback, to facilitate the user in re-formatting a new query. A knowledge base, which comprises a plurality of nodes depicting terminological concepts, is arranged to reflect conceptual proximity among the nodes. The information retrieval system processes the queries, identifies topics related to the query as well as query feedback terms, and then links both the topics and feedback terms to nodes of the knowledge base with corresponding terminological concepts. At least one focal node is selected from the knowledge base based on the topics to determine a conceptual proximity between the focal node and the query feedback nodes. The query feedback terms are ranked based on conceptual proximity to the focal node. A content processing system that identifies themes from a corpus of documents for use in query feedback processing is also disclosed.

Ranking of query feedback terms in an information retrieval system

A learning system learns terms in the context of a set of documents. During an accumulation phase, the learning system accumulates contextual data for a term in the form of a categorization schema. The categorization schema, which is based on a classification hierarchy, classifies the term in categories such that the classifications are based on uses of the terms in the set of documents During a computational phase, the learning system analyzes the categorization schema, and selects, if sufficient contextual data has been accumulated, a single category in the classification system to classify the term. A content processing system, which understands the thematic content of documents, is used in conjunction with the learning system.

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Scotts Valley, CA, United States of America

James Conklin