Knowledge discovery method with utility functions and feedback loops
First Claim
1. A method for performing knowledge discovery comprising the steps of:
- associating with each member of a data corpus one or more metatags through execution of a ranking function, wherein said ranking function is controllable through a parameter value;
selecting a first subset of members from said data corpus whose associated metatags are a match to a first set of criteria;
processing said first subset of members to produce a set of pairwise associations between elements of each of said first subset of members;
selecting a subset of said set of pairwise associations that reach a certain predefined or preset value;
identifying a second subset of members from said data corpus based on said subset of said pairwise associations;
computing a utility function to measure a utility of said second subset of members;
adjusting said parameter value to an adjusted parameter value based on said utility function;
associating one or more members of said data corpus with one or more retrospective metatags through execution of said ranking function controlled by said adjusted parameter value; and
selecting a third subset of members from said data corpus whose associated metatags are a match to a second set of criteria.
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Abstract
A knowledge discovery apparatus and method that extracts both specifically desired as well as pertinent and relevant information to query from a corpus of multiple elements that can be structured, unstructured, and/or semi-structured, along with imagery, video, speech, and other forms of data representation, to generate a set of outputs with a confidence metric-applied to the match of the output against the query. The invented apparatus includes a multi-level architecture, along with one or more feedback loop(s) from any Level N to any lower Level so that a user can control the output of this knowledge discovery method via providing inputs to the utility function.
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Citations
33 Claims
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1. A method for performing knowledge discovery comprising the steps of:
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associating with each member of a data corpus one or more metatags through execution of a ranking function, wherein said ranking function is controllable through a parameter value; selecting a first subset of members from said data corpus whose associated metatags are a match to a first set of criteria; processing said first subset of members to produce a set of pairwise associations between elements of each of said first subset of members; selecting a subset of said set of pairwise associations that reach a certain predefined or preset value; identifying a second subset of members from said data corpus based on said subset of said pairwise associations; computing a utility function to measure a utility of said second subset of members; adjusting said parameter value to an adjusted parameter value based on said utility function; associating one or more members of said data corpus with one or more retrospective metatags through execution of said ranking function controlled by said adjusted parameter value; and selecting a third subset of members from said data corpus whose associated metatags are a match to a second set of criteria. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 28, 29, 30, 31)
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12. A method for performing knowledge discovery, the method comprising the steps of:
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determining a first degree of correlation among a data corpus; metatagging members of said data corpus with metatags according to a metatagging scheme, wherein said metatagging scheme employs a first level of knowledge representation for said first degree of correlation and employs at least a second level of knowledge representation for a second degree of correlation among data, wherein said step of metatagging is controllable through a parameter value, and wherein said first and second levels of knowledge representation are representative of different degrees of correlation among data; determining said second degree of correlation among a first subset of said data corpus; identifying a second subset of members from said data corpus based on said second degree of correlation among said first subset of said data corpus; computing a utility function to measure a utility of said second subset of members; adjusting said parameter value to an adjusted parameter value based on said utility function; remetatagging said members of said data corpus with metatags according to said metatagging scheme, wherein said step of remetatagging is controlled through said adjusted parameter value and; selecting a third subset of members from said data corpus whose associated metatags are a match to a first set of criteria. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 32, 33)
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Specification