Systems and methods are provided for modifying a knowledge representation based on a machine-learning classifier. The knowledge representation is synthesized based on an object of interest. The machine-learning classifier is applied to predict relevance of validation data items. The knowledge representation is modified based on the results of the machine-learning classifier and the validation data. The modified knowledge representation can be used in subsequent applications of the classifier.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of modifying a knowledge representation based on a machine-learning classifier, the method comprising: receiving a knowledge representation encoded as a non-transitory computer-readable data structure, based on an object of interest, the knowledge representation comprising at least one concept and/or relationship between two or more concepts; receiving validation data, the validation data comprising a first set of one or more labeled content items having a label that classifies each content item into one or more categories including a first category known to be relevant to the object of interest and a second category known to not be relevant to the object of interest; predicting, with a machine-learning classifier that uses at least one attribute derived from the knowledge representation as a feature, each of the one or more labeled content items as one of: a) relevant to the object of interest or b) not relevant to the object of interest; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 2. The method of claim 1, further comprising synthesizing the knowledge representation based on contents of the object of interest. 3. The method of claim 2, wherein the synthesizing further comprises generating the at least one concept and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 4. The method of claim 1, wherein the knowledge representation includes weights associated with the at least one concept. 5. The method of claim 1, wherein the predicting is based on an intersection of the one or more labeled content items and the feature. 6. The method of claim 1, wherein the object of interest comprises a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and/or a search query. 7. The method of claim 1, further comprising: after modifying the knowledge representation: re-predicting each of the first set of one or more labeled content items using the modified knowledge representation; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 8. The method of claim 7, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of labeled content items in the first category is equal to or exceeds a precision threshold. 9. The method of claim 7, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is equal to or exceeds a recall threshold. 10. The method of claim 1, wherein modifying the knowledge representation comprises modifying weights associated with the at least one concept in the knowledge representation, and/or adding additional concepts to the knowledge representation. 11. The method of claim 1, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items in the first category is less than a threshold precision value. 12. The method of claim 1, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is less than a threshold recall value. 13. The method of claim 1, wherein the at least one attribute comprises at least one of: a total number of concepts intersecting between the knowledge representation and the one or more labeled content items, a number of broader concepts intersecting between the knowledge representation and the one or more labeled content items, a sum of weights of concepts intersecting between the knowledge representation and the one or more labeled content items, and/or a number of narrower concepts intersecting between the knowledge representation and the one or more labeled content items. 14. A system for modifying a knowledge representation based on a machine-learning classifier, the system comprising: at least one processor configured to perform a method comprising: receiving a knowledge representation encoded as a non-transitory computer-readable data structure, based on an object of interest, the knowledge representation comprising at least one concept and/or relationship between two or more concepts; receiving validation data, the validation data comprising a first set of one or more labeled content items having a label that classifies each content item into one or more categories including a first category known to be relevant to the object of interest and a second category known to not be relevant to the object of interest; predicting, with a machine-learning classifier that uses at least one attribute derived from the knowledge representation as a feature, each of the one or more labeled content items as one of: a) relevant to the object of interest or b) not relevant to the object of interest; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 15. The system of claim 14, wherein the method further comprises synthesizing the knowledge representation based on contents of the object of interest. 16. The system of claim 15, wherein the synthesizing further comprises generating the at least one concept and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 17. The system of claim 14, wherein the knowledge representation includes weights associated with the at least one concept. 18. The system of claim 14, wherein the predicting is based on an intersection of the one or more labeled content items and the feature. 19. The system of claim 14, wherein the object of interest comprises a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and/or a search query. 20. The system of claim 14, wherein the method further comprises: after modifying the knowledge representation: re-predicting each of the first set of one or more labeled content items using the modified knowledge representation; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 21. The system of claim 20, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of labeled content items in the first category is equal to or exceeds a precision threshold. 22. The system of claim 20, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is equal to or exceeds a recall threshold. 23. The system of claim 14, wherein modifying the knowledge representation comprises modifying weights associated with the at least one concept in the knowledge representation, and/or adding additional concepts to the knowledge representation. 24. The system of claim 14, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items in the first category is less than a threshold precision value. 25. The system of claim 14, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is less than a threshold recall value. 26. The system of claim 14, wherein the at least one attribute comprises at least one of: a total number of concepts intersecting between the knowledge representation and the one or more labeled content items, a number of broader concepts intersecting between the knowledge representation and the one or more labeled content items, a sum of weights of concepts intersecting between the knowledge representation and the one or more labeled content items, and/or a number of narrower concepts intersecting between the knowledge representation and the one or more labeled content items. 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 modifying a knowledge representation based on a machine-learning classifier, the method comprising: receiving a knowledge representation encoded as a non-transitory computer-readable data structure, based on an object of interest, the knowledge representation comprising at least one concept and/or relationship between two or more concepts; receiving validation data, the validation data comprising a first set of one or more labeled content items having a label that classifies each content item into one or more categories including a first category known to be relevant to the object of interest and a second category known to not be relevant to the object of interest; predicting, with a machine-learning classifier that uses at least one attribute derived from the knowledge representation as a feature, each of the one or more labeled content items as one of: a) relevant to the object of interest or b) not relevant to the object of interest; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 28. The at least one non-transitory computer readable storage medium of claim 27, wherein the method further comprises synthesizing the knowledge representation based on contents of the object of interest. 29. The at least one non-transitory computer readable storage medium of claim 28, wherein the synthesizing further comprises generating the at least one concept and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 30. The at least one non-transitory computer readable storage medium of claim 27, wherein the knowledge representation includes weights associated with the at least one concept. 31. The at least one non-transitory computer readable storage medium of claim 27, wherein the predicting is based on an intersection of the one or more labeled content items and the feature. 32. The at least one non-transitory computer readable storage medium of claim 27, wherein the object of interest comprises a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and/or a search query. 33. The at least one non-transitory computer readable storage medium of claim 27, wherein the method further comprises: after modifying the knowledge representation: re-predicting each of the first set of one or more labeled content items using the modified knowledge representation; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 34. The at least one non-transitory computer readable storage medium of claim 33, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of labeled content items in the first category is equal to or exceeds a precision threshold. 35. The at least one non-transitory computer readable storage medium of claim 33, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is equal to or exceeds a recall threshold. 36. The at least one non-transitory computer readable storage medium of claim 27, wherein modifying the knowledge representation comprises modifying weights associated with the at least one concept in the knowledge representation, and/or adding additional concepts to the knowledge representation. 37. The at least one non-transitory computer readable storage medium of claim 27, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items in the first category is less than a threshold precision value. 38. The at least one non-transitory computer readable storage medium of claim 27, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is less than a threshold recall value. 39. The at least one non-transitory computer readable storage medium of claim 27, wherein the at least one attribute comprises at least one of: a total number of concepts intersecting between the knowledge representation and the one or more labeled content items, a number of broader concepts intersecting between the knowledge representation and the one or more labeled content items, a sum of weights of concepts intersecting between the knowledge representation and the one or more labeled content items, and/or a number of narrower concepts intersecting between the knowledge representation and the one or more labeled content items.