METHODOLOGIES AND ANALYTICS TOOLS FOR LOCATING EXPERTS WITH SPECIFIC SETS OF EXPERTISE

• US 20080301105A1
• Filed: 06/05/2008
• Published: 12/04/2008
• Est. Priority Date: 02/13/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chen, Ying, Spangler, William Scott, Rhodes, James J., Kreulen, Jeffrey Thomas, Lelescu, Ana
• Granted Patent: US 8,577,834 B2
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A method and analytics tools for locating experts with specific sets of expertise are disclosed, the method including providing a collection of documents P₀; generating categories representing fields of expertise derived from the collection of documents P₀; refining the taxonomy of the categories by applying user domain knowledge; extracting structured fields from the collection of documents P₀; constructing a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and using the contingency table to identify a set of experts having a related expertise. The method may also include a network graph analysis that aids visualization of the relationship between people and expertise.

Claims

1. A method for use with a collection of documents P₀, the method comprising:

generating categories representing fields of expertise derived from the collection of documents P₀;

extracting structured fields from the collection of documents P₀;

constructing a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and

using the contingency table to identify a set of experts having a related expertise.

2. The method of claim 1, further comprising:

classifying the collection of documents P based on the extracted structured fields.

3. The method of claim 1, wherein:

said generating of categories includes generating a taxonomy from the collection of documents P.

4. The method of claim 3, further comprising:

refining the taxonomy by applying user domain knowledge.

5. The method of claim 1, wherein:

the structured fields include a structured name field including author names for the collection of documents P.

6. The method of claim 1, wherein:

said generating of categories includes generation of a feature space from text of the documents of P and comparison of the documents of P using a measure of distance in the feature space.

7. The method of claim 1, further comprising:

using trending information in the contingency table to identify a recent, with respect to a pre-determined time period, expertise.

8. A method for use with a set of seed documents P₀ extracted from a data warehouse, the method comprising:

searching the data warehouse to provide a set of additional documents P₁ similar to documents of P₀, wherein said similarity is determined using a statistical method;

generating an initial taxonomy for a combined document set P₀+P₁ that includes all documents from both the set of seed documents P₀ and the set of additional documents P₁; and

iterating the processes of extracting, searching, and generating using domain knowledge to produce a refined taxonomy from the initial taxonomy.

9. The method of claim 8, further comprising:

classifying the combined document set P₀+P₁ using terms obtained from structured fields, wherein the structured fields are extracted from the combined document set P₀+P₁.

10. The method of claim 8, further comprising:

using a name annotator to extract names from documents of the combined document set P₀+P₁ to form a structured field; and

classifying the combined document set P₀+P₁ using names obtained from the structured field.

11. The method of claim 8, wherein:

said generating of the initial taxonomy includes an analysis using words, bags of words, and phrases.

12. The method of claim 8, further comprising:

classifying the combined document set P₀+P₁ using structured fields;

using contingency analysis to generate a contingency table that compares categories of the refined taxonomy to the structured fields.

13. The method of claim 8, further comprising:

classifying the combined document set P₀+P₁ using structured fields, wherein at least one structured field includes names of people;

using contingency analysis to generate a contingency table that compares categories of the refined taxonomy to the structured fields; and

examining the contingency table to find a relationship between the categories and the names of people.

14. A computer program product comprising a computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to:

find a relationship between categories of a taxonomy and a collection of names of people derived from structured fields used to classify a set of documents from a data warehouse;

plot said relationship as a network graph; and

perform a network graph analysis to find a set of names from said collection of names of people that is most related to a set of said categories of said taxonomy.

15. The computer program product of claim 14, wherein:

said categories are categories of expertise.

16. The computer program product of claim 14, wherein:

each of said names of people is an author of a document from said set of documents.

17. The computer program product of claim 14, wherein:

said structured fields are used to identify a co-authoring relationship among the collection of names of people and the set of documents.

18. The computer program product of claim 14, wherein:

the computer finds said relationship using a contingency table generated from said categories and said collection of names of people; and

the computer plots said network graph from said contingency table.

19. The computer program product of claim 14, wherein:

the computer finds said relationship using a contingency table generated from said categories and said collection of names of people, each cell of said contingency table having an actual value;

the computer calculates a likelihood value for each cell; and

the computer determines a degree of significance for each cell based on a comparison of the actual value and the likelihood value for each cell.

20. A computer program product for use with a collection of documents P₀, the computer program product comprising a computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to:

generate categories representing fields of expertise derived from the collection of documents P₀;

extract structured fields from the collection of documents P₀;

construct a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and

identify, using the contingency table, a set of experts having a related expertise.