System and method for implementing a knowledge management system

• US 20070033221A1
• Filed: 10/05/2006
• Published: 02/08/2007
• Est. Priority Date: 06/15/1999
• Status: Abandoned Application

• Current Assignee: Knova Software Incorporated (Aptean Superior Holdings, Inc.)
• Original Assignee: Knova Software Incorporated (Aptean Superior Holdings, Inc.)
• Inventors: Kay, David, Angel, Mark, Copperman, Max, Rudy, Jeffrey, Fratkina, Raya, Huffman, Scott
• Application Number:
  US11/543,563
• Publication Number:
  US 20070033221A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99944 Object-oriented database structure
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

A method and system organize and retrieve information using taxonomies, a document classifier, and an autocontextualizer. Documents (or other knowledge containers) in an organization and retrieval subsystem may be manually or automatically classified into taxonomies. Documents are transformed from clear text into a structured record. Automatically constructed indexes help identify when the structured record is an appropriate response to a query. An automatic term extractor creates a list of terms indicative of the documents'"'"' subject matter. A subject matter expert identifies the terms relevant to the taxonomies. A term analysis system assigns the relevant terms to one or more taxonomies, and a suitable algorithm is then used to determine the relatedness between each list of terms and its associated taxonomy. The system then clusters documents for each taxonomy in accordance with the weights ascribed to the terms in the taxonomy'"'"'s list and a directed acyclic graph (DAG) structure is created.

Claims

1. A knowledge container, including:

an indication of an object; and

at least one tag, wherein each tag associates the object to at least one node in a knowledge map representation of a discrete perspective of a domain of knowledge.

2. The knowledge container of claim 1, wherein the object is one of content and resources.

3. The knowledge container of claim 1, further including administrative meta-data, comprised of structured information about the object.

4. The knowledge container of claim 1, wherein the indication of the object is the object itself.

5. The knowledge container of claim 1, wherein the indication of the object is a pointer to the object.

6. The knowledge container of claim 4, wherein the knowledge container includes:

marked content that is a textual representation of the object;

selective demarcation of regions of the textual representation of the object; and

a plurality of indicators of the nature of the content.

7. The knowledge container of claim 1, wherein each tag includes a weight indication representing a strength of association of the knowledge container to a particular node.

8. The knowledge container of claim 3, wherein the administrative metadata contains a description of the method used to assign the knowledge container to a particular node, including:

SME designation;

autocontextualization;

source mapping based on where the knowledge container came from; and

dialog response.

9. The knowledge container of claim 1, wherein said at least one tag is associated with nodes from a single taxonomy.

10. The knowledge container of claim 1, wherein said at least one tag is associated with nodes from a plurality of taxonomies.

11. The knowledge container of claim 1, wherein the object includes an indication of a person'"'"'s interests, information needs, and entitlements.

12. The knowledge container of claim 11, wherein the indication of the person'"'"'s interests, information needs, and entitlements includes a query for use by a retrieval method to retrieve objects mapped to the knowledge map.

13. The knowledge container of claim 11, wherein the tags for the knowledge container include a weight representing:

a strength of the person'"'"'s interest or information need;

relevancy to a question; and

expertise of a provider.

14. The knowledge container of claim 13, wherein the tags for the knowledge container associate the knowledge container with various portions of the knowledge map.

15. The knowledge container of claim 11, wherein the person'"'"'s entitlements are represented as tags to nodes of an entitlement taxonomy.

16. The knowledge container of claim 1, wherein the knowledge container is represented by a markup language such that it is displayable using template-based automated processing.

17. An autocontextualization method to automatically associate a knowledge container with a knowledge map having a plurality of taxonomies representative of selected discrete perspectives of a knowledge domain, each taxonomy having nodes corresponding to a conceptual area within the discrete perspective that the taxonomy represents, the autocontextualization method comprising:

using a feature recognizer to determine features of the knowledge container;

employing a classification system to classify the knowledge container based on the determined features;

generating a preliminary list of nodes to which the knowledge container may be associated; and

determining a weight indicating a strength of association therewith.

18. The autocontextualization method of claim 17, further including the steps of:

truncating nodes from the preliminary list based on the strength of association indicated by the weights; and

generating an indication that the remaining nodes are associated with the knowledge container.

19. The autocontextualization method of claim 17, further including:

following the classifying step, adjusting the weights determined by the classification system by applying an inference engine based on a set of rules regarding relationships between the nodes.

20. The autocontextualization method of claim 17, further including:

following the classifying step, adjusting the preliminary list of nodes generated by the classification system by applying an inference engine based on a set of rules regarding relationships between nodes.

21. The autocontextulization method of claim 17, wherein the feature recognizer recognizes as features at least some of:

dates;

times;

numbers;

monetary amounts;

people'"'"'s names;

organization names;

product names;

company names;

technical terminology;

noun phrases;

verb phrases; and

syntactic relationships.

22. The autocontextualization method of claim 17, wherein the step of generating a preliminary list of nodes further includes the step of identifying the features within the content most relied upon by the classifier in making the classification.

23. A method of identifying a knowledge container associated with a knowledge map, wherein the knowledge map includes at least one taxonomy representing a discrete perspective of a knowledge domain, wherein the at least one taxonomy is organized into a group of nodes, the nodes representing conceptual areas within the discrete perspective, and wherein the nodes have an indication of knowledge, including the particular content associated herewith, said method comprising:

processing information about a user to identify nodes in the taxonomy that represent conceptual areas previously indicated to be of interest to a user; and

performing a content-based retrieval over the knowledge containers associated with the nodes, to retrieve an ordered list of potentially relevant knowledge containers, where each retrieved knowledge container is assigned a numerical relevance score representing a quality of association between the retrieved knowledge container and the customer information.

24. The method of claim 23, wherein the information about the customer is processed automatically with any action by the user, and wherein at least one portion of the knowledge container of the re-ordered list is displayed to the user.