Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2001-05-08
2003-11-25
Chouless, Jack M. (Department: 2177)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C704S009000
Reexamination Certificate
active
06654740
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to a computer-based information retrieval and search engine to retrieve textual digital objects.
2. Description of the Related Art
With the explosive usage of the Internet, an efficient information retrieval system plays a most important role in processing with efficiency an ever increasing volume of digital textual objects such as web pages and documents. Many search engines based on the traditional query system of natural language processing are not robust enough because a considerable number of different words may often be used to describe the same meaning (synonymy), and more often than not the same word can be associated with different meanings (polysemy) so that traditional keywords-based retrieval systems are not robust, often missing related materials while recalling unrelated ones.
The principle of Latent Semantic Structure with truncated Singular Vector Decomposition (SVD) was introduced, as summarized in U.S. Pat. No. 4,839,853, so as to automatically construct a unified semantic space for retrieval. The truncated SVD is capable of capturing most of the important underlying structure in associating terms with documents, while at the same time removing the noise or possible variability in word usage which consistently plagues the word-based retrieval system. Each query from a user is projected onto a document fact space, and the collected documents having the closest projection to the query in the document fact space are selected for retrieval.
The basic postulate is that there is an underlying latent semantic structure in word usage data that is capable of capturing partially hidden or obscured aspects of the terms due to the variability of word choice inherent to the ambiguity of natural language.
SUMMARY OF THE INVENTION
In view of the foregoing, one object of the present invention is an information retrieval system having improved robustness in handling synonymy and polysemy.
Another object of the invention is an information retrieval system with improved search capabilities through reliance on differential latent document space and the exploitation of the differences between the two normalized document vectors of the documents.
A further object of the present invention is an information retrieval system capable of capturing an underlying latent semantic structure through a projection of the differences in word usage between two documents onto the differential latent semantic space.
The invention relates to an advanced information retrieval system of textual digital objects whereby full use is made of the projection of documents onto both of the reduced document space endowed with the singular value decomposition-based latent semantic structure and its orthogonal space. The new information retrieval and search engine system developed has an improved performance with textual digital objects for an immediate application in web document search over the Internet.
Rather than to directly compare the latent semantic vectors of documents and a query, which are actually their projections onto the LSI space, the advanced concept of differential latent document space is introduced into the present analysis, where differences between the two normalized document vectors of the documents are extensively exploited.
The basic postulate is that a projection onto the differential latent semantic space of the differences of word usage between the two documents is capable of capturing an underlying latent semantic structure.
Given a document, a document vector may be directly set up according to the terms of the document. Yet, there may be other document vectors that can just as well describe the document. For example, given a summarization method, a summary of a document could set up for the document, while a document vector of the summary could also be regarded as the document vector of the document. According to the present invention, there may be several representations of document vectors and each document is not constrained to being represented by one document vector only.
Because of the normalization of each document vector of the documents, the cosine measurement between a pair of document vectors may be measured by the length of differential document vector of the pair of documents.
A so-called interior differential covariant term-document matrix is set up where the columns of the matrix represent the differential document vectors of the same documents. Exploiting the singular vector decomposition method as set forth in
Numerical Recipes
, by Press, Flannery, Teukolsky and Vetterling (Cambridge University Press: Cambridge, England, 1986), the major left singular vectors associated with the largest singular values are selected as a major vector space, being called an interior differential latent semantic space, which is used to roughly describe the interior differential document vectors. Given a query to be regarded as a quasi-document, a best candidate document to be recalled from the documents should be selected from among those having a closest differential document vector of the query to the interior differential latent semantic space.
A so-called exterior differential covariant term-document matrix is set up where the columns of the matrix are differential document vectors of the different documents. Exploiting the singular vector decomposition method, the major left singular vectors associated with the largest singular values are selected as a major vector space, being called an exterior differential latent semantic space, which is used to roughly describe the exterior differential document vectors. Given a query to be regarded as a quasi-document, a best candidate document to be recalled from the documents should be selected from among those having a closest differential document vector of the query to the exterior differential latent semantic space.
Exploiting the concept of the interior and exterior differential term-document matrices, the present invention sets up a posteriori function based on the form of most likelihood functions as a possible measure of reliability in retrieving a document in the database by a query.
These and other objects of the invention, as well as many of the intended advantages thereof, will become more readily apparent when reference is made to the following description taken in conjunction with the accompanying drawings.
REFERENCES:
patent: 4839853 (1989-06-01), Deerwester et al.
patent: 5301109 (1994-04-01), Landauer et al.
patent: 6332138 (2001-12-01), Hull et al.
patent: 6510406 (2003-01-01), Marchisio
patent: 6523026 (2003-02-01), Gillis
Schetze, H. “Dimensions of Meaning,” Supercomputing '92, Proceedings 11-1992, PP. 787-796.*
Numerical Recipes in Fortran, The Art of Scientific Computing Second Edition, by Press, Flannery, Teukolsky and Vetterling (Cambridge University Press: Cambridge, England 1986).
Chen Liang
Sasai Hiroyuki
Tokuda Naoyuki
Chouless Jack M.
Jacobson & Holman PLLC
Sunflare Co., Ltd.
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