Systems and Methods for Labeling Sets of Objects

• US 20140280178A1
• Filed: 03/11/2014
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Citizennet Inc. (Advance Publications Incorporated)
• Original Assignee: Citizennet Inc. (Advance Publications Incorporated)
• Inventors: Benyamin, Daniel, Chu, Aaron
• Application Number:
  US14/204,206
• Publication Number:
  US 20140280178A1
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/285 Clustering or classification

Abstract

Systems and methods for labeling sets of objects using a taxonomy in accordance with embodiments of the invention are disclosed. In one embodiment, an object labeling server system includes a processor configured to obtain a set of object data including a set of keywords, score the object data based on a taxonomy including resource data, category information, relationships between the category information and resource data, and relationships between the category information, cluster the object data into groups of object data based on the scores, determine category data for at least one of the groups of object data based on the taxonomy, and generate a label for at least one of the groups of object data based on the determined category data for the group of object data.

Claims

1. An object labeling server system, comprising:

a processor; and

a memory connected to the processor and configured to store an object labeling application;

wherein the object labeling application configures the processor to;

obtain a set of object data, where the object data comprises a set of keywords;

score the object data in the set of object data based on a taxonomy, where the taxonomy comprises resource data, category information describing the resource data, relationships between the category information and resource data, and relationships between the category information;

cluster the object data into groups of object data based on the scores, where pieces of object data in a group of object data have similar scores;

determine category data for at least one of the groups of object data based on the taxonomy, where the category data comprises category information taken from the taxonomy based on the keywords associated with the pieces of object data within a group of object data; and

generate a label for at least one of the groups of object data based on the determined category data for the group of object data.

2. The object labeling server system of claim 1, wherein the object labeling application further configures the processor to discard outlier groups of object data.

3. The object labeling server system of claim 2, wherein an outlier group of object data has a size below a threshold value, where the size of the outlier group is based on the number of pieces of object data within the group of object data.

4. The object labeling server system of claim 2, wherein an outlier group of object data comprises at least one piece of object data having a score below a threshold value.

5. The object labeling server system of claim 1, wherein the object labeling application configures the processor to score the object data by determining the category intersection count for pairs of keywords in the object data by:

locating each keyword within the taxonomy; and

determining the number of categories in common for each pair of keywords.

6. The object labeling server system of claim 5, wherein the object labeling application configures the processor to determine the number of categories in common by:

locating a particular number of categories for each keyword within the taxonomy; and

counting the number of categories in common between the keywords.

7. The object labeling server system of claim 5, wherein:

the object labeling application configures the processor to determine the number of categories in common by progressively traversing the relationships between categories for each keyword until a common category is located; and

the category intersection count comprises the number of relationships traversed until a common category is reached.

8. The object labeling server system of claim 5, wherein the object labeling application configures the processor to determine the number of categories in common by measuring the average category level for a keyword by:

determining the category level for each intersecting category found in the determination of the category intersection count for a given keyword;

measuring the distance the category is from the keyword based on the taxonomy; and

dividing the sum of the category levels by the number of intersecting categories.

9. The object labeling system of claim 8, wherein the distance the category is from the keyword is based on the number of relationships between the keyword and the category within the taxonomy.

10. The object labeling server system of claim 1, wherein:

the object labeling application configures the processor to score the set of object data based on a category inverse document frequency of the keywords in the object data in the set of object data; and

the category inverse document frequency is a representation of the frequency of the keywords within the set of object data.

11. The object labeling server system of claim 10, wherein the object labeling application configures the processor to determine the category inverse document frequency of a keyword by:

determining if the keyword appears in a resource page;

dividing the total number of resources within the taxonomy corresponding to the keyword by the number of resources containing a relationship with category in the taxonomy; and

calculating the logarithm of the divided total number of resources.

12. The object labeling server system of claim 11, wherein determining if the keyword appears in a resource page is a Boolean value representing the appearance of the in the resource page.

13. The object labeling server system of claim 11, wherein the resource page is taken from an information source remote from the object labeling server system.

14. The object labeling server system of claim 1, wherein the object labeling application configures the processor to label the groups of object data by creating a composite description based on the determined category data for a particular group of object data.

15. The object labeling server system of claim 14, wherein the composite description is grammatically correct.

16. The object labeling server system of claim 1, wherein the object labeling application further configures the processor to disambiguate the set of object data by comparing one or more of the words within the set of words to a disambiguation database.

17. The object labeling server system of claim 16, wherein the disambiguation database comprises the taxonomy stored on the object labeling server system.

18. The object labeling server system of claim 16, wherein:

the disambiguation database comprises at least one information source that provides a disambiguation service; and

the information source is separate from the object labeling server system.

19. The object labeling server system of claim 16, wherein:

the disambiguation of a set of keywords within the set of object data depends on the context of the keywords within the set of object data; and

the object labeling application configures the processor to determine the context of the keywords based on the grammatical context of the keyword within the set of object data.

20. The object labeling server system of claim 19, wherein the disambiguation of a keyword includes substituting an updated keyword taken from the disambiguation database for the keyword.

21. The object labeling server system of claim 19, wherein the disambiguation of a keyword includes adding additional keywords taken from the disambiguation database to the set of object data.

22. A method for labeling sets of objects, comprising:

obtaining a set of object data using an object labeling server system, where the object data comprises a set of keywords;

scoring the object data in the set of object data based on a taxonomy using the object labeling server system, where the taxonomy comprises resource data, category information describing the resource data, relationships between the category information and resource data, and relationships between the category information;

clustering the object data into group of object data based on the scores using the object labeling server system, where pieces of object data in a group of object data have similar scores;

determining category data for at least one of the groups of object data based on the taxonomy using the object labeling server system, where the category data comprises category information taken from the taxonomy based on the keywords associated with the pieces of object data within a group of object data; and

generating a label for at least one of the groups of object data based on the determined category data for the group of object data using the object labeling server system.