A system, apparatus, and method are provided for implementing secondary profiles for members of an online application or service. Each member has a corresponding primary profile populated by the member, and a secondary profile populated with information from data sources other than the member. Each fact or entry in the secondary (or inferred) profile is accompanied by a confidence score reflecting confidence in the source of the fact, confidence that the fact is correctly associated with this member, and/or other factors. A given fact may be obtained or extracted from multiple sources, with each copy or version assigned a separate confidence score. In response to a request to identify members having a particular attribute, in addition to identifying members that have the attribute in their primary profiles, members having the attribute in their secondary profiles may be identified if the corresponding confidence scores are greater than a threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: populating primary profiles of members of a user community with information provided by the corresponding members; extracting a plurality of facts from multiple data sources; and for each fact, with one or more computers: for each of one or more members, generating a confidence score representing a confidence that the fact is associated with the member; and for each member having a confidence score above a threshold, adding the fact to a secondary profile of the member; wherein each member's secondary profile is populated without requiring action by the corresponding member. 2. The method of claim 1, wherein generating the confidence score for the fact comprises: assembling a fact feature vector comprising attributes of the fact and/or the data source from which the fact was extracted; and applying a machine learning model to compare the fact feature vector with member feature vectors representing one or more members of the user community. 3. The method of claim 2, wherein: a member feature vector representing a given member of the user community comprises attributes of the given member's primary profile. 4. The method of claim 3, wherein the member feature vector representing the given member further comprises attributes of primary profiles of one or more connections of the given member within the user community. 5. The method of claim 3, wherein the attributes of the given member's primary profile include two or more of: name; summary; position; education; and skills. 6. The method of claim 3, wherein the attributes of the given member's primary profile include one or more of: patents; projects; and publications. 7. The method of claim 2, wherein: for each of the multiple data sources, a different machine learning model is applied for facts extracted from the data source. 8. The method of claim 1, further comprising for at least a first fact: modifying the generated confidence scores based on a credibility score associated with the data source from which the first fact was extracted. 9. The method of claim 1, further comprising: identifying a first fact extracted from two or more different data sources, wherein first confidence scores generated for a first member associated with the first fact, for each of the two or more data sources, differ; and aggregating the first confidence scores to yield a final confidence score. 10. The method of claim 1, further comprising, for each fact added to a given secondary profile of a given member: recording the confidence score associated with the given member and the given fact; and recording the data source of the given fact. 11. An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: populate primary profiles of members of a user community with information provided by the corresponding members; extract a plurality of facts from multiple data sources; and for each fact: for each of one or more members, generate a confidence score representing a confidence that the fact is associated with the member; and for each member having a confidence score above a threshold, add the fact to a secondary profile of the member; wherein each member's secondary profile is populated without requiring action by the corresponding member. 12. The apparatus of claim 11, wherein generating the confidence score for the fact comprises: assembling a fact feature vector comprising attributes of the fact and/or the data source from which the fact was extracted; and applying a machine learning model to compare the fact feature vector with member feature vectors representing one or more members of the user community. 13. The apparatus of claim 12, wherein a member feature vector representing a given member of the user community comprises: attributes of the given member's primary profile; and attributes of primary profiles of one or more connections of the given member within the user community. 14. The apparatus of claim 13, wherein the attributes of the given member's primary profile include two or more of: name; summary; position; education; and skills. 15. The apparatus of claim 11, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to, for at least a first fact: modify the generated confidence scores based on a credibility score associated with the data source from which the first fact was extracted. 16. The apparatus of claim 11, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to: identify a first fact extracted from two or more different data sources, wherein first confidence scores generated for a first member associated with the first fact, for each of the two or more data sources, differ; and aggregate the first confidence scores to yield a final confidence score. 17. A system, comprising: a profile module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to populate primary profiles of members of a user community with information provided by the corresponding members; an extraction module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to extract a plurality of facts from multiple data sources; and a correlation module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to, for each fact: for each of one or more members, generate a confidence score representing a confidence that the fact is associated with the member; and for each member having a confidence score above a threshold, add the fact to a secondary profile of the member; wherein each member's secondary profile is populated without requiring action by the corresponding member. 18. The system of claim 17, wherein generating the confidence score for the fact comprises: assembling a fact feature vector comprising attributes of the fact and/or the data source from which the fact was extracted; and applying a machine learning model to compare the fact feature vector with member feature vectors representing one or more members of the user community. 19. The system of claim 18, wherein: a member feature vector representing a given member of the user community comprises: attributes of the given member's primary profile; and attributes of primary profiles of one or more connections of the given member within the user community. 20. The system of claim 19, wherein the attributes of the given member's primary profile include two or more of: name; summary; position; education; and skills.