Concept Categorization

• US 20150134667A1
• Filed: 07/31/2012
• Published: 05/14/2015
• Est. Priority Date: 07/31/2012
• Status: Abandoned Application

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Micro Focus LLC (Open Text Corporation)
• Inventors: Hou, Hui-Man, Chen, Lijiang, Chen, Shimin, Jiang, Peng
• Application Number:
  US14/397,640
• Publication Number:
  US 20150134667A1
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/35 Clustering; Classification

Abstract

Systems, methods, and computer-readable and executable instructions are provided for categorizing a concept. Categorizing a concept can include selecting a target concept with a number of surrounding textual contexts. Categorizing a concept can also include determining a number of candidate categories for the target concept based on the number of surrounding textual contexts. Categorizing a concept can also include selecting a predefined number of articles, each with a desired relatedness to the number of candidate categories. Furthermore, categorizing a concept can include calculating a relatedness score for each of the number of candidate categories based on a relatedness with the number of articles.

Claims

1. A method for categorizing concepts, comprising:

selecting a target concept with a number of surrounding textual contexts from an article;

determining a number of candidate categories for the target concept based on the number of surrounding textual contexts;

selecting a number of additional articles, each with a desired relatedness to the number of candidate categories; and

calculating a relatedness score for each of the number of candidate categories based on a relatedness with the number of articles.

2. The method of claim 1, wherein selecting the number of additional articles includes eliminating a number of articles with a number of links below a predetermined threshold.

3. The method of claim 1, wherein selecting the number of additional articles includes eliminating a number of articles exceeding a predetermined threshold.

4. The method of claim 3, wherein eliminating articles exceeding the predetermined threshold includes calculating the relatedness between each article and a number of other articles in the number of candidate categories.

5. The method of claim 1, wherein calculating the relatedness score includes supplementing a number of numerical values for a candidate category if the number of articles are below a predetermined threshold.

6. The method of claim 5, wherein the supplemented number of articles have a score that is equal to a lowest relatedness score article.

7. A non-transitory machine-readable medium storing a set of instructions executable by a processor to cause a computer to:

determine a number of candidate categories for a target concept based on a number of surrounding textual contexts;

split each of the number of candidate categories into a number of sub-component categories;

calculate a relatedness between each of the number of sub-component categories and the target concept; and

rank the number of candidate categories based on the relatedness between each of the number of sub-component categories and the target concept.

8. The medium of claim 7, wherein the sub-component categories are filtered to eliminate a bias.

9. The medium of claim 7, further comprising a set of instructions to rank the number of candidate categories based on a desired sub-component relatedness and a relatedness of the candidate categories with a number of articles.

10. The medium of claim 7, wherein the number of sub-component categories include a number of variant names for each of the number of candidate categories.

11. The medium of claim 7, wherein each of the number of sub-component categories include an article.

12. A computing system for categorizing a concept, comprising:

a memory resource;

a processing resource coupled to the memory resource to implement;

a candidate category determination module to determine a number of candidate categories for a target concept based on a number of surrounding textual contexts;

an article selection module to select a first number of articles, each with a desired relatedness to the number of candidate categories;

the candidate category determination module to split each of the number of candidate categories into a number of sub-component names, wherein the sub-component names correspond to a second number of articles;

the article selection module to select a desired number of articles from the first number of articles and a desired sub-component name from the number of sub-component names; and

a calculation module to calculate a ranking of a relatedness of the number of candidate categories to the target concept based on a combined calculated relatedness of;

the first number of articles and the target concept; and

the second number of articles that correspond to the desired sub-component and the target concept.

13. The computing system of claim 12, wherein the combined calculated relatedness utilizes a predetermined number of articles with an average relatedness of the first number of articles and the target concept.

14. The computing system of claim 12, wherein the combined calculated relatedness utilizes a predetermined number of articles with a maximum relatedness of the second number of articles and the target concept.

15. The computing system of claim 12, wherein the relatedness is calculated utilizing a number of common links.