Keyword associative document retrieval system

• US 5,297,042 A
• Filed: 10/05/1990
• Issued: 03/22/1994
• Est. Priority Date: 10/05/1989
• Status: Expired due to Fees

• Current Assignee: Ricoh Company Limited
• Original Assignee: Ricoh Company Limited
• Inventors: Morita, Tetsuya
• Primary Examiner(s): Envall, Jr., Roy N.
• Assistant Examiner(s): Poinvil, Frantzy
• Application Number:
  US07/593,817
• Time in Patent Office: 1,264 Days
• Field of Search: 382/13-15, 395/20, 395/24-27, 364/419, 364/200 MS File, 364/900 MS File
• US Class Current: 707/765
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: Y10S 707/959 Network
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A document retrieval system includes an inputting unit for inputting a retrieval condition including one or a plurality of keywords and a weight value for each keyword, an operating unit having first factors corresponding to relationship values, each relationship value being defined as a degree of the relationship between two keywords out of keywords which are predetermined in the document retrieval system and second factors corresponding to importance values, each importance value being defined as a degree of importance of a keyword in each one of a plurality of documents which are predetermined in the document retrieval system, the operation unit generating a relevance value, which represents a degree of relevance in satisfying a user'"'"'s requirement, for each of the documents on the basis of the retrieval condition input from the inputting unit, the first factors and the second factors, and an outputting unit for outputting the relevance value for each of the documents as a retrieval result.

Claims

1. A document retrieval system comprising:

a) inputting means for inputting one or a plurality of keywords and weight value for each keyword;

b) a neural network including;

1) an input layer having units corresponding to keywords that have been previously determined in said document retrieval system,

2) a hidden layer having the same number of units as said input layer, and

3) an output layer having units corresponding to documents that have been previously determined in said document retrieval system;

wherein;

i) each of the units in said input layer is linked to each of the units in said hidden layer by first link factors determined based on relationship values;

ii) each relationship value is defined as a degree of a relationship between two keywords corresponding to linked units in said input and hidden layers;

iii) each of the units in said hidden layer is linked to each of the units in said output layer by second link factors determined based on importance values;

iv) each importance value is defined as a degree of importance of one of said keywords in one of said documents, the keyword and the document corresponding to linked units in said hidden and output layers;

v) the weight value for each of the keywords is supplied from said inputting means to a corresponding one of the units in said input layer; and

vi) an output value for each of the document is output from a corresponding one of the units in said output layer; and

c) means, coupled to said neural network, for generating a relevance value for each of the documents based on the output value from a corresponding one of the units in said output layer of said neural network and for outputting the relevance value as a retrieval result, said relevance value being defined as a degree of relevance in satisfying a user'"'"'s requirement.

2. A document retrieval system as claimed in claim 1, wherein each of the relationship values is determined by use of a cooccurrence frequency which represents a total number of times that both of the two keywords occur in each of the documents.

3. A document retrieval system as claimed in claim 1, wherein a relationship value R_kj between a k-th keyword and a j-th-keyword is determined on the basis of the following formula;

##EQU5## where N_kj represents the total number of times that the k-th keyword occurs with the j-th keyword in each of the documents, N_k represents the total number of times that the k-th keyword occurs in each of the documents, and N_j represents the total number of times that the j-th keyword occurs in each of the documents.

4. A document retrieval system as claimed in claim 1, wherein each of the importance values is determined by the frequency with which the keyword occurs in each of the documents.

5. A document retrieval system as claimed in claim 1, wherein a first link factor between a k-th unit in said input layer and a j-th unit in said hidden layer is determined on the basis of said relationship value regarding a k-th keyword and a j-th keyword.

6. A document retrieval system as claimed in claim 1, wherein a second link factor between a j-th unit in said hidden layer and an i-th unit in said output layer is determined on the basis of said importance value of a j-th keyword in an i-th document.

7. A document retrieval system as claimed in claim 1 further comprising learning means including:

means for changing said first link factors between the units in said input layer and the units in said hidden layer; and

means for changing said second link factors between the units in said hidden layer and the units in said output layer;

wherein the changing performed within the learning means is performed so as to cause said relevance value for each of the documents to approach a value input by the user.

8. A document retrieval system as claimed in claim 7, wherein said learning means includes:

a) expected value inputting means for inputting an expected value which corresponds to a relevance value for each of the documents required by the user; and

b) changing means for changing (1) said first link factors between the units in said input layer and the units in said hidden layer and (2) said second link factors between the units in the hidden layer and the units in said output layer on the basis of said expected value for each document, so as to cause said relevance value for each of the documents to approach the expected value input by said user.

9. A document retrieval system as claimed in claim 8, wherein said changing means includes means for changing said first link factors between the units in the input layer and the units in said hidden layer and said second link factors between the units in said hidden layer and the units in said output layer in accordance with a gradient descent method.