System and Method for Determining Semantically Related Terms

• US 20090037399A1
• Filed: 07/31/2007
• Published: 02/05/2009
• Est. Priority Date: 07/31/2007
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Bartz, Kevin, Murthi, Vijay, Sebastian, Shaji
• Application Number:
  US11/831,311
• Publication Number:
  US 20090037399A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri

Abstract

Systems and methods for determining semantically related terms are disclosed. Generally, a semantically related term tool trains a model to predict a degree of relevance between a candidate term and one or more seed terms. The model may be trained based on data such as a plurality of seed sets, a plurality of semantically related term sets, and a plurality of modular optimized dynamic sets (“MODS”), where each semantically related term set is related to a seed set of the plurality of seed sets and each MODS is related to a seed set of the plurality of seed sets. The semantically related term tool then determines a plurality of terms that are semantically related to one or more terms in a new seed set based on the model, the one or more terms in the seed set, and a plurality of candidate terms.

Claims

1. A method for determining semantically related terms, the method comprising:

training a model to predict a degree of relevance between a candidate term and one or more seed terms, wherein the model is trained based on a plurality of seed sets, a plurality of semantically related term sets, and a plurality of modular optimized dynamic sets (“

MODS”

), and wherein each semantically related term set is related to a seed set of the plurality of seed sets and each MODS is related to a seed set of the plurality of seed sets; and

determining a plurality of terms that are semantically related to one or more terms in a seed set based on the model, the one or more terms in the seed set, and a plurality of candidate terms.

2. The method of claim 1, wherein a semantically related term tool creates the plurality of semantically related terms sets based on the plurality of seed sets.

3. The method of claim 1, wherein a MODS module creates the plurality of MODS based on the plurality of seed sets.

4. The method of claim 1, wherein the terms in the seed set are received from one of an Internet search engine, an online advertisement service provider, and a website provider.

5. The method of claim 1, further comprising:

suggesting at least one term of the plurality of terms to a user.

6. The method of claim 1, further comprising:

exporting at least one term of the plurality of terms to one of an online advertisement service provider and an Internet search engine.

7. The method of claim 1, wherein determining a plurality of terms that are semantically related to one or more terms in a seed set comprises:

for each candidate term of the plurality of candidate terms, determining a degree of relevance between the candidate term and the one or more terms of the seed set based on the model; and

identifying a subset of the plurality of candidate terms based on the determined degrees of relevance.

8. The method of claim 7, wherein identifying the subset comprises:

identifying candidate terms of the plurality of candidate terms associated with a determined degree of relevance above a predetermined threshold.

9. The method of claim 7, wherein identifying the subset comprises:

identifying a number of terms with the largest determined degrees of relevance.

10. A computer-readable storage medium comprising a set of instructions for determining semantically related terms, the set of instructions to direct a processor to perform acts of:

training a model to predict a degree of relevance between a candidate term and one or more seed terms, wherein the model is trained based on a plurality of seed sets, a plurality of semantically related term sets, and a plurality of modular optimized dynamic sets (“

MODS”

), and wherein each semantically related term set is related to a seed set of the plurality of seed sets and each MODS is related to a seed set of the plurality of seed sets; and

determining a plurality of terms that are semantically related to one or more terms in a seed set based on the model, the one or more terms in the seed set, and a plurality of candidate terms.

11. The computer-readable storage medium of claim 10, wherein determining a plurality of terms that are semantically to one or more terms in a seed set comprises:

for each candidate term of the plurality of candidate terms, determining a degree of relevance between the candidate term and the one or more terms of the seed set based on the model; and

identifying a subset of the plurality of candidate terms based on the determined degrees of relevance.

12. The computer-readable storage medium of claim 11, wherein identifying the subset comprises:

identifying candidate terms of the plurality of candidate terms associated with a determined degree of relevance above a predetermined threshold.

13. The computer-readable storage medium of claim 11, wherein identifying the subset comprises:

identifying a number of terms with the largest determined degrees of relevance.

14. A system for determining semantically related terms, the system comprising:

a semantically related term tool operative to train a model to predict a degree of relevance between a candidate term and one or more seed terms, and to determine a plurality of terms that are semantically related to one or more terms in a seed set based on the model, the one or more terms of the seed set, and a plurality of candidate terms;

wherein the semantically related term tool trains the model based on a plurality of seed sets, a plurality of semantically related term sets, and a plurality of modular optimized dynamic sets (“

MODS”

), and wherein each semantically related term set is related to a seed set of the plurality of seed sets and each MODS is related to a seed set of the plurality of seed sets.

15. The system of claim 14, wherein the semantically related term tool is further operative to identify candidate terms of the plurality of candidate terms associated with a determined degree of relevance above a predetermined threshold.

16. The system of claim 14, wherein the semantically related term tool is further operative to identify a number of terms with the largest determined degrees of relevance.

17. The system of claim 14, wherein the semantically related term tool is further operative to suggest at least a portion of the determined plurality of terms to a user.

18. The system of claim 14, wherein the semantically related term tool is further operative to export at least a portion of the determined plurality of terms to at least one of an Internet search engine and an online advertisement service provider.