Automated adaptive classification system for knowledge networks

• US 7,730,072 B2
• Filed: 12/29/2000
• Issued: 06/01/2010
• Est. Priority Date: 04/14/2000
• Status: Expired due to Fees

• Current Assignee: Oracle OTC Subsidiary LLC (Oracle Corporation)
• Original Assignee: RightNow Technologies Incorporated (Oracle Corporation)
• Inventors: Richter, James Neal, Prabu, Ganesh, Warner, Douglas K.
• Primary Examiner(s): Mofiz; Apu M
• Assistant Examiner(s): CHEN, TE Y
• Application Number:
  US09/751,934
• Publication Number:
  US 20010044791A1
• Time in Patent Office: 3,441 Days
• Field of Search: 706/11, 706/45, 706/52, 707/3, 707501-514103 R, 707/104.1, 707/10, 707/1, 709/201, 709203-223
• US Class Current: 707/748
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 18/00 Pattern recognition

Abstract

A method and apparatus for use in an information retrieval system is provided, which derives related informational items that have a usage based relationship strength, and which results in an efficient and more accurate dynamic relationship association between informational items. This system comprises the steps for detecting a selection of at least a first informational item and a second informational item in an information retrieval session. A relationship type is assigned based on characteristic similarities between the first informational item and the second informational item. Additionally, a relationship strength is assigned based on historical frequency of the consecutive selection of the first and second informational items and providing an access to the second informational item upon detection of the first being accessed by a user of the information retrieval system. Also, in accordance with the principles of the present invention, the extraction of textual database fields; the application of multiple text classification algorithms; the merging of the algorithm results; the encoding of the merged results as a Bayesian-type link; the use of feedback methods to weight, prune and age the relationship link serves to automate and enhance the process of classification in an information retrieval system.

Claims

1. A computer readable storage medium having a computer program stored thereon for implementing a mapping method of classifying a plurality of informational items in an information retrieval system having a database, said computer program comprising a set of instructions for implementing said method comprising the steps of:

identifying a first informational item, wherein said first informational item includes one of a first frequently asked question or other data;

identifying a second informational item, wherein said second informational item includes one of a second frequently asked question or other data;

dynamically creating a general relationship field with initial integer weight values to define similarities of respective characteristics between said first informational item and said second informational item in an information retrieval session, wherein said field is used for representing an automated data item classification process;

integrating said classification process and said informational items with a combination of data aging and pruning feedback algorithms to produce a weighted output simulating a non-conventional Bayesian-type Belief Network;

detecting an access of said first informational item;

detecting an access of said second informational item;

establishing the presence or absence of a relationship link between said first informational item and said second informational item;

refining said initial integer weight values of said general relationship field associated with said first and second informational items based on a historical frequency of said relationship link being accessed by a user, wherein said initial integer weight values are changed as said frequency increases and on the prior presence or absence of said relationship link; and

storing said weighted output being at least partially indicative of the classification of said informational items.

2. The computer readable storage medium as recited in claim 1 wherein said steps of identifying and detecting the second informational item includes identifying and detecting of a plurality of informational items.

3. The computer readable storage medium as recited in claim 2, further comprising the step of:

applying said algorithm for data aging wherein the usage of said relationship link is monitored and used as feed back for said initial integer weight value associated with said relationship link.

4. The computer readable storage medium as recited in claim 3, further comprising the step of:

applying said pruning algorithm wherein external information regarding the usefulness of at least one relationship link is utilized to modify said initial integer weight value or existence of a recorded relationship link.

5. The computer readable storage medium as recited in claim 4, wherein said pruning algorithm performs the removal of irrelevant relationship links subsequent to the data aging feedback process.

6. The computer readable storage medium as recited in claim 4, wherein said pruning algorithm makes use of a user determined feedback of the usefulness of a relationship link.

7. A computer readable storage medium having stored thereon a computer program for implementing a method of classifying a plurality of information items in an information retrieval system, said computer program comprising a set of instructions for implementing the steps recited in claim 2.

8. The computer readable storage medium according to claim 7, wherein said computer program further comprises one or more instructions for clustering the resulting output of said algorithms into a knowledge network.

9. The computer readable storage medium according to claim 7, wherein said computer program further comprises one or more instructions for improving the usefulness of said relationship links through weighting of said relationship links.

10. The computer readable storage medium according to claim 7, wherein said computer program further comprises one or more instructions for improving the usefulness of said relationship links through pruning of said relationship links.

11. The computer readable storage medium according to claim 7, wherein said computer program further comprises one or more instructions for improving the usefulness of said relationship links through aging of said relationship links.

12. The computer readable storage medium according to claim 7, wherein said computer program further comprises one or more instructions for improving the usefulness of said relationship links through weighting, pruning and aging of said relationship links.

13. The computer readable storage medium as recited in claim 2, wherein said plurality of algorithms are used and wherein said initial integer weight value of said relationship link is adjusted in direct proportion to the number of said algorithms used to determine the existence of said relationship link.

14. The computer readable storage medium as recited in claim 2, wherein said relationship link is positioned in a list in direct proportion to the degree of consensus among said algorithms.

15. The computer readable storage medium as recited in claim 2, wherein each algorithm used runs independently of all other algorithms.

16. The computer readable storage medium as recited in claim 2, further comprising the step of merging the outputs of said algorithms.

17. The computer readable storage medium as recited in claim 2, further comprising the step of recording said relationship link in said non Bayesian-type Belief Network.

18. The computer readable storage medium as recited in claim 3, wherein said algorithm for data aging runs as a function of traffic load to age the relationship links according to relevance of the relationship links.

19. A computer readable storage medium having a computer program stored thereon for providing classification of informational items in an information retrieval system having a database, said computer program having a network structure which allows cycles comprising:

means for detecting the access of informational items, wherein said informational items include one of a frequently asked question or other data;

means for dynamically creating a general relationship field with initial integer weight values to define similarities of respective characteristics between a first informational item and a second informational item in an information retrieval session, wherein said field is used for representing an automated data item classification process;

means for integrating said data item classification process and said informational items with the combination of data aging and pruning feedback algorithms to produce a weighted output simulating a non-conventional Bayesian-type Belief Network;

means for establishing the presence or absence of relationship links between said informational items to enhance the effectiveness of said information retrieval system;

means for refining said initial integer weight values of said general relationship field associated with said informational items based on a historical frequency of said relationship links being accessed by a user, wherein said initial integer weight values are changed as said frequency increases and on the prior presence or absence of said relationship links;

means for storing said relationship links and said weighted outputs.

20. The computer readable storage medium of claim 19 including:

means for aging said relationship links; and

means for pruning said relationship links.

21. The computer readable storage medium of claim 20 including means for merging the resulting output of said algorithms into a knowledge network.

22. A computer readable storage medium having a computer program stored thereon for implementing a mapping method of classifying a plurality of informational items in an information retrieval system having a database, said computer program comprising a set of instructions for implementing said method comprising the steps of:

detecting an access of a first informational item, wherein said first informational item includes one of a frequently asked question or other data;

detecting an access of a second informational item, wherein said second informational item includes one of a frequently asked question or other data;

establishing the presence or absence of a relationship link between said first informational item and said second informational item;

dynamically creating a general relationship field with initial integer weight values to define similarities of respective characteristics between said first informational item and said second informational item in an information retrieval session, wherein said field is used for representing an automated data item classification process;

integrating said classification process and said informational items with the combination of data aging and pruning feedback algorithms to produce a weighted output simulating a non-conventional Bayesian-type Belief Network;

refining said initial integer weight values of said general relationship field associated with said first and second informational items based on a historical frequency of said relationship link being accessed by a user, wherein said initial integer weight values are changes as said frequency increases and on the prior presence or absence of said relationship link; and

storing said weighted output being at least partially indicative of the classification of said informational items.