Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information. The method further comprises obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept, wherein the first concept in a semantic network is represented by a data structure storing any data associated with a node in the semantic network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of searching for content in a target set of content based on a reference set of content, a reference knowledge representation representing knowledge associated with the reference set of content, and a target knowledge representation representing knowledge associated with the target set of content, the method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target knowledge representation and the reference knowledge representation; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the knowledge representation is represented by a data structure storing data associated with a node in the knowledge representation. 2. The computer-implemented method of claim 1, wherein obtaining the at least one concept semantically relevant to the first search query comprises: constructing a merged knowledge representation based on the reference knowledge representation and the target knowledge representation; identifying or generating a first concept in the merged knowledge representation, the first concept representing the user-specified search query; obtaining the at least one concept, including a second concept, semantically relevant to the first concept at least in part by synthesizing the second concept based on the first concept and at least one other concept in the merged knowledge representation. 3. The computer-implemented method of claim 2, wherein constructing the merged knowledge representation comprises merging the reference knowledge representation and the target knowledge representation around a concept the reference knowledge representation and the target knowledge representation have in common. 4. The computer-implemented method of claim 2, wherein constructing the merged knowledge representation comprises pruning one or more concepts from the reference knowledge representation and/or from the target knowledge representation prior to the merging. 5. The computer-implemented method of claim 2, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique. 6. The computer-implemented method of claim 2, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation. 7. The computer-implemented method of claim 2, wherein obtaining the at least one concept comprises: obtaining a plurality of concepts semantically relevant to the first concept; computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and selecting the at least one concept based on the scores computed for the one or more concepts. 8. The computer-implemented method of claim 7, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity. 9. The computer-implemented method of claim 2, wherein identifying or generating the first concept comprises: determining whether the user-specified search query matches an identifier of a concept in the merged knowledge representation; and when it is determined that the user-specified search query does not match an identifier of a concept in the merged knowledge representation, generating the first concept in the merged knowledge representation. 10. The computer-implemented method of claim 1, further comprising receiving user context information associated with the user, wherein the user context information comprises at least one of demographic information associated with the user, information from the user's browsing history, information typed in by the user, and/or information highlighted by the user. 11. The computer-implemented method of claim 1, wherein the target knowledge representation is 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 and an edge between two nodes represents a relationship between the two corresponding concepts. 12. The computer-implemented method of claim 1, wherein the target set of content comprises content accessible through a website of a business. 13. The computer-implemented method of claim 1, wherein the reference set of content comprises content accessible through an online information repository. 14. A system for searching for content in a target set of content based on a reference set of content, a reference knowledge representation representing knowledge associated with the reference set of content, and a target knowledge representation representing knowledge associated with the target set of content, the system comprising: at least one processor configured to perform a method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target knowledge representation and the reference knowledge representation; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the knowledge representation is represented by a data structure storing data associated with a node in the knowledge representation. 15. The system of claim 14, wherein obtaining the at least one concept semantically relevant to the first search query comprises: constructing a merged knowledge representation based on the reference knowledge representation and the target knowledge representation; identifying or generating a first concept in the merged knowledge representation, the first concept representing the user-specified search query; obtaining the at least one concept, including a second concept, semantically relevant to the first concept at least in part by synthesizing the second concept based on the first concept and at least one other concept in the merged knowledge representation. 16. The system of claim 15, wherein constructing the merged knowledge representation comprises merging the reference knowledge representation and the target semantic around a concept the reference knowledge representation and the target knowledge representation have in common. 17. The system of claim 15, wherein constructing the merged knowledge representation comprises pruning one or more concepts from the reference knowledge representation and/or from the target knowledge representation prior to the merging. 18. The system of claim 15, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique. 19. The system of claim 15, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation. 20. The system of claim 15, wherein obtaining the at least one concept comprises: obtaining a plurality of concepts semantically relevant to the first concept; computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and selecting the at least one concept based on the scores computed for the one or more concepts. 21. The system of claim 20, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity. 22. The system of claim 15, wherein identifying or generating the first concept comprises: determining whether the user-specified search query matches an identifier of a concept in the merged knowledge representation; and when it is determined that the user-specified search query does not match an identifier of a concept in the merged knowledge representation, generating the first concept in the merged knowledge representation. 23. The system of claim 14, further comprising receiving user context information associated with the user, wherein the user context information comprises at least one of demographic information associated with the user, information from the user's browsing history, information typed in by the user, and/or information highlighted by the user. 24. The system of claim 14, wherein the target knowledge representation is 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 and an edge between two nodes represents a relationship between the two corresponding concepts. 25. The system of claim 14, wherein the target set of content comprises content accessible through a website of a business. 26. The system of claim 14, wherein the reference set of content comprises content accessible through an online information repository. 27. 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 of searching for content in a target set of content based on a reference set of content, a reference knowledge representation representing knowledge associated with the reference set of content, and a target knowledge representation representing knowledge associated with the target set of content, the method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target knowledge representation and the reference knowledge representation; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the knowledge representation is represented by a data structure storing data associated with a node in the knowledge representation. 28. The at least one non-transitory computer-readable storage medium of claim 27, wherein obtaining the at least one concept semantically relevant to the first search query comprises: constructing a merged knowledge representation based on the reference knowledge representation and the target knowledge representation; identifying or generating a first concept in the merged knowledge representation, the first concept representing the user-specified search query; obtaining the at least one concept, including a second concept, semantically relevant to the first concept at least in part by synthesizing the second concept based on the first concept and at least one other concept in the merged knowledge representation. 29. The at least one non-transitory computer-readable storage medium of claim 28, wherein constructing the merged knowledge representation comprises merging the reference knowledge representation and the target semantic around a concept the reference knowledge representation and the target knowledge representation have in common. 30. The at least one non-transitory computer-readable storage medium of claim 28, wherein constructing the merged knowledge representation comprises pruning one or more concepts from the reference knowledge representation and/or from the target knowledge representation prior to the merging. 31. The at least one non-transitory computer-readable storage medium of claim 28, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique. 32. The at least one non-transitory computer-readable storage medium of claim 28, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation. 33. The at least one non-transitory computer-readable storage medium of claim 28, wherein obtaining the at least one concept comprises: obtaining a plurality of concepts semantically relevant to the first concept; computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and selecting the at least one concept based on the scores computed for the one or more concepts. 34. The at least one non-transitory computer-readable storage medium of claim 33, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity. 35. The at least one non-transitory computer-readable storage medium of claim 28, wherein identifying or generating the first concept comprises: determining whether the user-specified search query matches an identifier of a concept in the merged knowledge representation; and when it is determined that the user-specified search query does not match an identifier of a concept in the merged knowledge representation, generating the first concept in the merged knowledge representation. 36. The at least one non-transitory computer-readable storage medium of claim 27, further comprising receiving user context information associated with the user, wherein the user context information comprises at least one of demographic information associated with the user, information from the user's browsing history, information typed in by the user, and/or information highlighted by the user. 37. The at least one non-transitory computer-readable storage medium of claim 27, wherein the target knowledge representation is 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 and an edge between two nodes represents a relationship between the two corresponding concepts. 38. The at least one non-transitory computer-readable storage medium of claim 27, wherein the target set of content comprises content accessible through a website of a business. 39. The at least one non-transitory computer-readable storage medium of claim 27, wherein the reference set of content comprises content accessible through an online information repository. 40. The computer-implemented method of claim 1, wherein the reference knowledge representation and the target knowledge representation are separate and a merged knowledge representation is not constructed. 41. The system of claim 14, wherein the reference knowledge representation and the target knowledge representation are separate and a merged knowledge representation is not constructed. 42. The at least one non-transitory computer-readable storage medium of claim 27, wherein the reference knowledge representation and the target knowledge representation are separate and a merged knowledge representation is not constructed. 43. The computer-implemented method of claim 1, wherein the reference knowledge representation is a reference semantic network, and the target knowledge representation is a target semantic network. 44. The system of claim 14, wherein the reference knowledge representation is a reference semantic network, and the target knowledge representation is a target semantic network. 45. The at least one non-transitory computer-readable storage medium of claim 27, wherein the reference knowledge representation is a reference semantic network, and the target knowledge representation is a target semantic network.