ASSIGNING INTO ONE SET OF CATEGORIES INFORMATION THAT HAS BEEN ASSIGNED TO OTHER SETS OF CATEGORIES

• US 20110137908A1
• Filed: 12/28/2010
• Published: 06/09/2011
• Est. Priority Date: 03/10/2006
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Han, Hui, Dom, Byron Edward, Balasubramanyan, Ramnath, Pavlov, Dmitry Yurievich
• Application Number:
  US12/980,162
• Publication Number:
  US 20110137908A1
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/353 into predefined classes

Abstract

Techniques are described for assigning, to target categories of a target scheme, items that have been obtained from a plurality of sources. In situations in which one or more of the sources has organized its information according to a source scheme that differs from the target scheme, the assignment may be based, in part, on an estimate of the probability that items from a particular source category should be assigned to a particular target category. Such probability estimates may be based on how many training set items associated with the particular source category have been assigned to the particular target category. Source categories may be grouped into clusters. The probability estimates may also be based on how many training set items within the cluster to which the particular source category has been mapped, have been assigned the particular target category.

Claims

1. A method comprising:

automatically assigning items to target categories in a target scheme;

wherein the items assigned to the target categories in the target scheme include a first set of items;

wherein items in the first set of items have previously been assigned to source categories in a first source scheme that is different from said target scheme;

wherein the first set of items includes a particular item;

wherein the step of assigning items to categories in the target scheme includes;

receiving information about the particular item;

determining, from the information, a source category to which the particular item was assigned in the first source scheme;

determining, based on a source-category-to-cluster mapping, a cluster to which said particular source category has been mapped, and

assigning the particular item to a target category based, in part, on the cluster to which the particular source category has been mapped;

wherein the method is performed by one or more computing devices.

2. The method of claim 1 wherein the step of assigning the particular item to a target category based, in part, on the cluster to which the particular source category has been mapped includes:

determining a set of probability values, wherein each probability value in the set of probability values corresponds to a target category and represents an estimate of the probability that items associated with categories that map to said cluster should be assigned to said corresponding target category; and

assigning the particular item to a target category based, in part, on said set of probability values.

3. The method of claim 2 wherein:

the source-category-to-cluster mapping maps source categories to clusters; and

the clusters include at least one cluster to which source categories from multiple source schemes have been mapped.

4. The method of claim 2 further comprising:

determining whether a cluster associated with a new item is a non-determinative cluster; and

if the cluster associated with the new item is a non-determinative cluster, then assigning the new item to a target category without estimating the probability that items associated with source categories that map to said cluster should be assigned to each target category.

5. The method of claim 2 further comprising:

determining whether a cluster associated with a new item is an insufficiently sampled cluster; and

if the cluster associated with the new item is an insufficiently sampled cluster, then assigning the new item to a target category without estimating the probability that items associated with said source categories that map to said cluster should be assigned to each target category.

6. A non-transitory computer-readable medium storing instructions, which, when executed by one or more processors, cause one or more computing devices to perform operations comprising:

automatically assigning items to target categories in a target scheme;

wherein the items assigned to the target categories in the target scheme include a first set of items;

wherein items in the first set of items have previously been assigned to source categories in a first source scheme that is different from said target scheme;

wherein the first set of items includes a particular item;

wherein assigning items to categories in the target scheme includes;

receiving information about the particular item;

determining, from the information, a source category to which the particular item was assigned in the first source scheme;

determining, based on a source-category-to-cluster mapping, a cluster to which said particular source category has been mapped, and

assigning the particular item to a target category based, in part, on the cluster to which the particular source category has been mapped.

7. The non-transitory computer-readable medium of claim 6 wherein instructions for assigning the particular item to a target category based, in part, on the cluster to which the particular source category has been mapped includes instructions for:

determining a set of probability values, wherein each probability value in the set of probability values corresponds to a target category and represents an estimate of the probability that items associated with categories that map to said cluster should be assigned to said corresponding target category; and

assigning the particular item to a target category based, in part, on said set of probability values.

8. The non-transitory computer-readable medium of claim 7 wherein:

the source-category-to-cluster mapping maps source categories to clusters; and

the clusters include at least one cluster to which source categories from multiple source schemes have been mapped.

9. The non-transitory computer-readable medium of claim 7 wherein the instructions, when executed by one or more processors, further cause the one or more computing devices to perform operations comprising:

determining whether a cluster associated with a new item is a non-determinative cluster; and

if the cluster associated with the new item is a non-determinative cluster, then assigning the new item to a target category without estimating the probability that items associated with source categories that map to said cluster should be assigned to each target category.

10. The non-transitory computer-readable medium of claim 7 wherein the instructions, when executed by one or more processors, further cause the one or more computing devices to perform operations comprising:

determining whether a cluster associated with a new item is an insufficiently sampled cluster; and

if the cluster associated with the new item is an insufficiently sampled cluster, then assigning the new item to a target category without estimating the probability that items associated with said source categories that map to said cluster should be assigned to each target category.

11. A system comprising:

one or more processors;

logic comprising instructions which, when executed by the one or more processors, cause the system to perform operations comprising;

automatically assigning items to target categories in a target scheme;

wherein the items assigned to the target categories in the target scheme include a first set of items;

wherein items in the first set of items have previously been assigned to source categories in a first source scheme that is different from said target scheme;

wherein the first set of items includes a particular item;

wherein the assigning items to categories in the target scheme includes;

receiving information about the particular item;

determining, from the information, a source category to which the particular item was assigned in the first source scheme;

determining, based on a source-category-to-cluster mapping, a cluster to which said particular source category has been mapped, and

assigning the particular item to a target category based, in part, on the cluster to which the particular source category has been mapped.

12. The system of claim 11 wherein instructions for assigning the particular item to a target category based, in part, on the cluster to which the particular source category has been mapped includes instructions for:

determining a set of probability values, wherein each probability value in the set of probability values corresponds to a target category and represents an estimate of the probability that items associated with categories that map to said cluster should be assigned to said corresponding target category; and

assigning the particular item to a target category based, in part, on said set of probability values.

13. The system of claim 12 wherein:

the source-category-to-cluster mapping maps source categories to clusters; and

the clusters include at least one cluster to which source categories from multiple source schemes have been mapped.

14. The system of claim 12 wherein the instructions, when executed by one or more processors, further cause the system to perform operations comprising:

determining whether a cluster associated with a new item is a non-determinative cluster; and

if the cluster associated with the new item is a non-determinative cluster, then assigning the new item to a target category without estimating the probability that items associated with source categories that map to said cluster should be assigned to each target category.

15. The system of claim 12 wherein the instructions, when executed by one or more processors, further cause the system to perform operations comprising:

determining whether a cluster associated with a new item is an insufficiently sampled cluster; and

if the cluster associated with the new item is an insufficiently sampled cluster, then assigning the new item to a target category without estimating the probability that items associated with said source categories that map to said cluster should be assigned to each target category.