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United States Patent

[19]

Wical

US005940821A [ii] Patent Number: [45] Date of Patent:

5,940,821 Aug. 17,1999

[54] INFORMATION PRESENTATION IN A KNOWLEDGE BASE SEARCH AND RETRIEVAL SYSTEM

[75] Inventor: Kelly Wical, San Carlos, Calif.

[73] Assignee: Oracle Corporation, Redwood Shores, Calif.

[21] Appl. No.: 08/861,650 [22] Filed: May 21, 1997

[51] Int. CI.6 G06F 17/30

[52] U.S. CI 707/3; 707/4; 707/5

[58] Field of Search 707/1, 2, 3, 4,

707/5

[56] References Cited

U.S. PATENT DOCUMENTS

4,332,014 5/1982 Nakazawa et al 364/900

4,719,571 1/1988 Rissanen et al 364/300

4,823,306 4/1989 Barbie et al 364/900

4,972,349 11/1990 Kleinberger 364/900

5,159,667 10/1992 Borrey et al 395/148

5,167,011 11/1992 Priest 395/54

5,201,404 4/1993 Maki et al 707/3

5,257,185 10/1993 Farley et al 364/419.19

5,276,616 1/1994 Kuga et al 364/419.08

5,278,980 1/1994 Pederson et al 707/3

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5,325,298 6/1994 Gallant 364/419.19

5,347,623 9/1994 Takano et al 395/157

5,369,763 11/1994 Biles 395/600

5,418,946 5/1995 Mori 707/3

5,434,961 7/1995 Horiuchi et al 395/144

5,442,780 8/1995 Takanashi et al 395/600

5,463,773 10/1995 Sakakibara et al 707/3

5,497,489 3/1996 Menne 707/3

5,519,865 5/1996 Kondo et al 707/3

5,535,382 7/1996 Ogawa 707/3

5,598,557 1/1997 Doner et al 395/605

5,625,767 4/1997 Bartell et al 395/140

5,630,117 5/1997 Oren et al 395/602

5,630,125 5/1997 Zellweger 395/614

5,687,364 11/1997 Saund et al 707/505

5,768,580 6/1998 Wical 707/102

5,802,504 9/1998 Suda et al 706/11

OTHER PUBLICATIONS

Cox, John '"Text-Analysis' Server to Simplify Queries",
Communications Week, Apr. 19, 1993 pp. 1-3.

Primary Examiner—-Thomas G. Black
Assistant Examiner—Frantz Coby

Attorney, Agent, or Firm—Fleisler, Dubb, Meyer & Lovejoy
LLP

[57] ABSTRACT

A knowledge base search and retrieval system, which includes factual knowledge base queries and concept knowledge base queries, is disclosed. A knowledge base stores associations among terminology/categories that have a lexical, semantical or usage association. Document theme vectors identify the content of documents through themes as well as through classification of the documents in categories that reflects what the documents are primarily about. The factual knowledge base queries identify, in response to an input query, documents relevant to the input query through expansion of the query terms as well as through expansion of themes. The concept knowledge base query does not identify specific documents in response to a query, but specifies terminology that identifies the potential existence of documents in a particular area.

43 Claims, 22 Drawing Sheets

I Generate senses and distinct parts from query |—400

T ,

I Generate query term strengths k~402

r

I Expand query terms using knowledge base |-^~ 405

Select categories in knowledge base
identified by expanded query terms

i .

Select documents classified for those categories 1^420

T \

I Select themes from documents |—430

I Sort and compile information by theme I—^ 440

T

I List themes in order of strongest themes I— 450

_

Select top themes from additional documents based on predetermined criteria

I .

Organize themes in groups L~ 4^5

T ,

| Order theme groups k~470

I Order documents within groups I— 475

7;

Display groups and associated document names I—480 I Display categories classified for documents 485

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