Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating training data for 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 a first set of content items, wherein the first set comprises one or more content items without a label, wherein the label classifies a content item into one or more categories; determining one or more scores for one or more respective content items of the first set, wherein the score for a respective content item is based on the knowledge representation and contents of the respective content item; and generating the training data for the machine learning classifier by assigning a label to the one or more respective content items of the first set based on the score associated with the one or more respective content items of the first set. 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 a weight associated with the at least one concept. 5. The method of claim 1, wherein the score for the respective content item is based on an intersection of the at least one concept in the knowledge representation and the contents of the respective content item. 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: training a classifier to predict a label for one or more unclassified content items based on the labels assigned to the first set of content items and one or more features associated with the first set of content items. 8. The method of claim 7, further comprising: receiving a second set of content items, wherein the second set comprises one or more content items without a label; and assigning, by the classifier, a label to one or more of the content items of the second set based on one or more features associated with the respective one or more content items of the second set. 9. The method of claim 1, wherein assigning the label to the one or more respective content items of the first set comprises assigning the label based on the score for a respective content item of the first set exceeding a predetermined threshold. 10. The method of claim 1, wherein the label is one of “recommend” or “do not recommend”. 11. The method of claim 7, wherein the one or more features comprise at least one of: title, length, author, term frequency, and/or inverse document frequency. 12. The method of claim 7, wherein the one or more features comprise one or more attributes of the knowledge representation. 13. A system for generating training data for 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 a first set of content items, wherein the first set comprises one or more content items without a label, wherein the label classifies a content item into one or more categories; determining one or more scores for one or more respective content items of the first set, wherein the score for a respective content item is based on the knowledge representation and contents of the respective content item; and generating the training data for the machine learning classifier by assigning a label to the one or more respective content items of the first set based on the score associated with the one or more respective content items of the first set. 14. The system of claim 13, wherein the method further comprises synthesizing the knowledge representation based on contents of the object of interest. 15. The system of claim 14, wherein the synthesizing further comprises generating the at least one concepts and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 16. The system of claim 13, wherein the knowledge representation includes weights associated with the at least one concept. 17. The system of claim 13, wherein the score for the respective content item is based on an intersection of the at least one concept in the knowledge representation and the contents of the respective content item. 18. The system of claim 13, 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. 19. The system of claim 13, wherein the method further comprises training a classifier to predict a label for one or more unclassified content items based on the labels assigned to the first set of content items and one or more features associated with the first set of content items. 20. The system of claim 19, wherein the method further comprises: receiving a second set of content items, wherein the second set comprises one or more content items without a label; and assigning, by the classifier, a label to one or more of the content items of the second set based on one or more features associated with the respective one or more content items of the second set. 21. The system of claim 13, wherein assigning the label to the one or more respective content items of the first set comprises assigning the label based on the score for a respective content item of the first set exceeding a predetermined threshold. 22. The system of claim 13, wherein the label is one of “recommend” or “do not recommend”. 23. The system of claim 19, wherein the one or more features comprise at least one of title, length, author, term frequency, and/or inverse document frequency. 24. The system of claim 19, wherein the one or more features comprise one or more attributes of the knowledge representation. 25. 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 generating training data for 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 a first set of content items, wherein the first set comprises one or more content items without a label, wherein the label classifies a content item into one or more categories; determining one or more scores for one or more respective content items of the first set, wherein the score for a respective content item is based on the knowledge representation and contents of the respective content item; and generating the training data for the machine learning classifier by assigning a label to the one or more respective content items of the first set based on the score associated with the one or more respective content items of the first set. 26. The at least one non-transitory computer readable storage medium of claim 25, wherein the method further comprises synthesizing the knowledge representation based on contents of the object of interest. 27. The at least one non-transitory computer readable storage medium of claim 26, 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. 28. The at least one non-transitory computer readable storage medium of claim 25, wherein the knowledge representation includes weights associated with the at least one concept. 29. The at least one non-transitory computer readable storage medium of claim 25, wherein the score for the respective content item is based on an intersection of the at least one concept in the knowledge representation and the contents of the respective content item. 30. The at least one non-transitory computer readable storage medium of claim 25, 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. 31. The at least one non-transitory computer readable storage medium of claim 25, wherein the method further comprises training a classifier to predict a label for one or more unclassified content items based on the labels assigned to the first set of content items and one or more features associated with the first set of content items. 32. The at least one non-transitory computer readable storage medium of claim 31, wherein the method further comprises: receiving a second set of content items, wherein the second set comprises one or more content items without a label; and assigning, by the classifier, a label to one or more of the content items of the second set based on one or more features associated with the respective one or more content items of the second set. 33. The at least one non-transitory computer readable storage medium of claim 25, wherein assigning the label to the one or more respective content items of the first set comprises assigning the label based on the score for a respective content item of the first set exceeding a predetermined threshold. 34. The at least one non-transitory computer readable storage medium of claim 25, wherein the label is one of “recommend” or “do not recommend”. 35. The at least one non-transitory computer readable storage medium of claim 31, wherein the one or more features comprise at least one of: title, length, author, term frequency, and/or inverse document frequency. 36. The at least one non-transitory computer readable storage medium of claim 31, wherein the one or more features comprise one or more attributes of the knowledge representation.