Inverse inference engine for high performance web search
First Claim
1. An information retrieval method comprising the steps of:
- generating a term-document matrix to represent electronic information files stored in a computer system, each element in said term-document matrix indicating a number of occurrences of a term within a respective one of said electronic information files;
generating, responsive to said term-document matrix, a term-spread matrix, wherein said term spread matrix is a weighted autocorrelation of said term-document matrix, said term-spread matrix indicating an amount of variation in term usage in the information files and, also, the extent to which terms are correlated;
receiving a user query from a user, said user query consisting of at least one term;
in response to said user query, generating a user query vector, wherein said user query vector has as many elements as the rows of the term-spread matrix;
generating, responsive to said user query vector, an error-covariance matrix, wherein said error-covariance matrix reflects an expected degree of uncertainty in the initial choice of keywords of said user;
formulating, responsive to said term-spread matrix, error-covariance matrix, and user query vector, a constrained optimization problem, wherein the choice of a lambda value equal to a Lagrange multiplier value in said constrained optimization problem determines the extent of a trade-off between a degree of fit and the stability of all solutions to said constrained optimization problem;
generating, responsive to said constrained optimization problem, a solution vector including a plurality of document weights, each one of said plurality of document weights corresponding to one of each said information files, wherein each of said document weights reflects a degree of correlation between said user query and the corresponding one of said information files; and
providing an information response to said user reflecting said document weights.
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Abstract
An information retrieval system that deals with the problems of synonymy, polysemy, and retrieval by concept by allowing for a wide margin of uncertainty in the initial choice of keywords in a query. For each input query vector and an information matrix, the disclosed system solves an optimization problem which maximizes the stability of a solution at a given level of misfit. The disclosed system may include a decomposition of the information matrix in terms of orthogonal basis functions. Each basis encodes groups of conceptually related keywords. The bases are arranged in order of decreasing statistical relevance to a query. The disclosed search engine approximates the input query with a weighted sum of the first few bases. Other commercial applications than the disclosed search engine can also be built on the disclosed techniques.
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Citations
22 Claims
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1. An information retrieval method comprising the steps of:
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generating a term-document matrix to represent electronic information files stored in a computer system, each element in said term-document matrix indicating a number of occurrences of a term within a respective one of said electronic information files;
generating, responsive to said term-document matrix, a term-spread matrix, wherein said term spread matrix is a weighted autocorrelation of said term-document matrix, said term-spread matrix indicating an amount of variation in term usage in the information files and, also, the extent to which terms are correlated;
receiving a user query from a user, said user query consisting of at least one term;
in response to said user query, generating a user query vector, wherein said user query vector has as many elements as the rows of the term-spread matrix;
generating, responsive to said user query vector, an error-covariance matrix, wherein said error-covariance matrix reflects an expected degree of uncertainty in the initial choice of keywords of said user;
formulating, responsive to said term-spread matrix, error-covariance matrix, and user query vector, a constrained optimization problem, wherein the choice of a lambda value equal to a Lagrange multiplier value in said constrained optimization problem determines the extent of a trade-off between a degree of fit and the stability of all solutions to said constrained optimization problem;
generating, responsive to said constrained optimization problem, a solution vector including a plurality of document weights, each one of said plurality of document weights corresponding to one of each said information files, wherein each of said document weights reflects a degree of correlation between said user query and the corresponding one of said information files; and
providing an information response to said user reflecting said document weights. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification