Methods and apparatus for identifying concepts corresponding to input information

1. A method comprising:

obtaining user context information associated with a user, wherein the user context information comprises a first portion and a second portion different from the first portion, wherein the user context information comprises text, wherein the first portion comprises at least a first word in the text, and wherein the second portion comprises a remainder of the words in the text not included in said first portion; and

identifying a first concept associated with a meaning of the first portion from a first group of concepts in a knowledge representation (KR), wherein each concept in the first group of concepts is associated with a different meaning of the first portion,

wherein the identifying is performed at least in part by using at least one processor, and said identifying is based on a graph of the knowledge representation, and the second portion,

wherein the identifying the first concept comprises calculating a measure of semantic coherence between the first concept and a second concept associate with a meaning of the second portion from a second group of concepts in the KR by using the graph of the KR, and

wherein calculating the measure of semantic coherence between the first concept and the second concept comprises:

identifying a first semantic context of the first concept in the KR, wherein the first semantic context is defined as a number of concepts in the first group of concepts which are within a predetermined distance of said first concept in said KR;

identifying a second semantic context of the second concept in the KR, wherein the second semantic context is defined as a number of concepts in the second group of concepts which are within a predetermined distance of the second concept in said KR; and

calculating the measure of semantic coherence based on whether said first semantic context intersects with said second semantic context, wherein said first semantic context intersects with said second semantic context when said first semantic context and said second semantic context share at least a predetermined number of concepts.

2. The method of claim 1 , further comprising:

identifying the second concept associated with the meaning of the second portion from a second group of concepts in the KR, wherein each concept in the second group of concepts is associated with a different meaning of the second portion.

3. The method of claim 1 , further comprising:

constructing a context intersection graph using the graph of the KR;

calculating a score for each concept in the first group of concepts at least in part by using the context intersection graph; and

identifying the first concept as a concept in the first group of concepts having the highest score.

4. The method of claim 3 , wherein the context intersection graph comprises a plurality of nodes including a first node for the first concept and a second node for the second concept, and wherein constructing the context intersection graph comprises:

when the measure of semantic coherence between the first concept and the second concept is above a threshold, adding an edge between the first node and the second node to the context intersection graph, wherein the edge has a weight equal to the measure of semantic coherence.

5. The method of claim 3 , wherein the score for the first concept is calculated based at least in part on a measure of dominance of the first concept.

6. The method of claim 5 , wherein the score for the first concept is further calculated based on at least one weight of at least one edge incident to a first node for the first concept in the context intersection graph.

7. The method of claim 1 , wherein the knowledge representation is a semantic network represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept in the knowledge representation and an edge between two nodes represents a relationship between concepts associated with the two nodes.

8. The method of claim 1 , wherein the user context information comprises at least one of a search query provided by the user, demographic information about the user, information from the user's browsing history, information typed by the user, and/or information highlighted by the user.

9. The method of claim 1 , wherein the user context information comprises a search query provided by a user.

10. The method of claim 1 , further comprising:

providing output information to the user, wherein the output information is obtained by using the identified first concept.

11. The method of claim 10 , wherein the output information comprises one or more advertisements and/or one or more product recommendations.

12. The method of claim 10 , wherein the output information comprises content accessible through a website.

13. A system comprising:

at least one processor configured to perform:

obtaining user context information associated with a user, wherein the user context information comprises a first portion and a second portion different from the first portion, wherein the user context information comprises text, wherein the first portion comprises at least a first word in the text, and wherein the second portion comprises a remainder of the words in the text not included in said first portion; and

identifying a first concept associated with a meaning of the first portion from a first group of concepts in a knowledge representation (KR), wherein each concept in the first group of concepts is associated with a different meaning of the first portion,

wherein the identifying is performed at least in part by using a graph of the knowledge representation and the second portion,

wherein the identifying the first concept comprises calculating a measure of semantic coherence between the first concept and a second concept associate with a meaning of the second portion from a second group of concepts in the KR by using the graph of the KR, and

wherein calculating the measure of semantic coherence between the first concept and the second concept comprises:

identifying a first semantic context of the first concept in the KR, wherein the first semantic context is defined as a number of concepts in the first croup of concepts which are within a predetermined distance of said first concept in said KR;

identifying a second semantic context of the second concept in the KR, wherein the second semantic context is defined as a number of concepts in the second croup of concepts which are within a predetermined distance of the second concept in said KR; and

calculating the measure of semantic coherence based on whether said first semantic context intersects with said second semantic context, wherein said first semantic context intersects with said second semantic context when said first semantic context and said second semantic context share at least a predetermined number of concepts.

14. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising:

obtaining user context information associated with a user, wherein the user context information comprises a first portion and a second portion different from the first portion, wherein the user context information comprises text, wherein the first portion comprises at least a first word in the text, and wherein the second portion comprises a remainder of the words in the text not included in said first portion; and

identifying a first concept associated with a meaning of the first portion from a first group of concepts in a knowledge representation (KR), wherein each concept in the first group of concepts is associated with a different meaning of the first portion,

wherein the identifying is performed at least in part by using a graph of the knowledge representation, and the second portion,

wherein the identifying the first concept comprises calculating a measure of semantic coherence between the first concept and a second concept associate with a meaning of the second portion from a second group of concepts in the KR by using the graph of the KR, and

wherein calculating the measure of semantic coherence between the first concept and the second concept comprises:

identifying a first semantic context of the first concept in the KR, wherein the first semantic context is defined as a number of concepts in the first group of concepts which are within a predetermined distance of said first concept in said KR;

identifying a second semantic context of the second concept in the KR, wherein the second semantic context is defined as a number of concepts in the second group of concepts which are within a predetermined distance of the second concept in said KR; and

calculating the measure of semantic coherence based on whether said first semantic context intersects with said second semantic context, wherein said first semantic context intersects with said second semantic context when said first semantic context and said second semantic context share at least a predetermined number of concepts.

15. A method comprising:

obtaining user context information associated with a user, wherein the user context information comprises a first portion and a second portion different from the first portion, wherein the user context information comprises text, wherein the first portion comprises at least a first word in the text, and wherein the second portion comprises a remainder of the words in the text not included in said first portion; and

disambiguating between a first concept in a knowledge representation (KR) associated with a first meaning of the first portion and a second concept in the KR associated with a second meaning of the first portion,

wherein the disambiguating is performed at least in part by using at least one processor, a graph of the knowledge representation, and the second portion,

wherein the identifying the first concept comprises calculating a measure of semantic coherence between the first concept and a second concept associate with a meaning of the second portion from a second group of concepts in the KR by using the graph of the KR, and

wherein calculating the measure of semantic coherence between the first concept and the second concept comprises:

identifying a first semantic context of the first concept in the KR, wherein the first semantic context is defined as a number of concepts in the first group of concepts which are within a predetermined distance of said first concept in said KR;

identifying a second semantic context of the second concept in the KR, wherein the second semantic context is defined as a number of concepts in the second croup of concepts which are within a predetermined distance of the second concept in said KR; and

calculating the measure of semantic coherence based on whether said first semantic context intersects with said second semantic context, wherein said first semantic context intersects with said second semantic context when said first semantic context and said second semantic context share at least a predetermined number of concepts.

16. A system comprising:

at least one processor configured to perform:

obtaining user context information associated with a user, wherein the user context information comprises a first portion and a second portion different from the first portion, wherein the user context information comprises text, wherein the first portion comprises at least a first word in the text, and wherein the second portion comprises a remainder of the words in the text not included in said first portion; and

disambiguating between a first concept in a knowledge representation (KR) associated with a first meaning of the first portion and a second concept in the KR associated with a second meaning of the first portion,

wherein the disambiguating is performed at least in part by using a graph of the knowledge representation and the second portion,

wherein the identifying the first concept comprises calculating a measure of semantic coherence between the first concept and a second concept associate with a meaning of the second portion from a second group of concepts in the KR by using the graph of the KR, and

wherein calculating the measure of semantic coherence between the first concept and the second concept comprises:

identifying a first semantic context of the first concept in the KR, wherein the first semantic context is defined as a number of concepts in the first croup of concepts which are within a predetermined distance of said first concept in said KR;

identifying a second semantic context of the second concept in the KR, wherein the second semantic context is defined as a number of concepts in the second croup of concepts which are within a predetermined distance of the second concept in said KR; and

calculating the measure of semantic coherence based on whether said first semantic context intersects with said second semantic context, wherein said first semantic context intersects with said second semantic context when said first semantic context and said second semantic context share at least a predetermined number of concepts.

17. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising:

obtaining user context information associated with a user, wherein the user context information comprises a first portion and a second portion different from the first portion, wherein the user context information comprises text, wherein the first portion comprises at least a first word in the text, and wherein the second portion comprises a remainder of the words in the text not included in said first portion; and

disambiguating between a first concept in a knowledge representation (KR) associated with a first meaning of the first portion and a second concept in the KR associated with a second meaning of the first portion,

wherein the disambiguating is performed at least in part by using a graph of the knowledge representation and the second portion,

wherein the identifying the first concept comprises calculating a measure of semantic coherence between the first concept and a second concept associate with a meaning of the second portion from a second group of concepts in the KR by using the graph of the KR, and

wherein calculating the measure of semantic coherence between the first concept and the second concept comprises:

identifying a first semantic context of the first concept in the KR, wherein the first semantic context is defined as a number of concepts in the first croup of concepts which are within a predetermined distance of said first concept in said KR;

identifying a second semantic context of the second concept in the KR, wherein the second semantic context is defined as a number of concepts in the second croup of concepts which are within a predetermined distance of the second concept in said KR; and

calculating the measure of semantic coherence based on whether said first semantic context intersects with said second semantic context, wherein said first semantic context intersects with said second semantic context when said first semantic context and said second semantic context share at least a predetermined number of concepts.