Abstract: Embodiments provide for assessing and scoring user proficiency in topics determined by data from social networks and other sources. Embodiments can combine the information available to direct models, e.g., using user profiles, endorsements, etc., with data from various other external knowledge bases to find implicit topics for users, and other types of reasoning to compute proficiency scores. Implicit topics, i.e., those topics related to explicitly identified areas of expertise, can be determined based on a topic graph such as a Directed Acyclic Graph (DAG). Generally speaking, embodiments can traverse the topic graph for explicitly identified skills or topics and determine related or similar new skills based on nearby nodes of the graph. This approach can uncover skills that user that may not have disclosed as well as scoring users on skills based on the skill's similarity to those the user did claim.

Abstract: Embodiments provide for assessing and scoring user proficiency in topics determined by data from social networks and other sources. Embodiments can combine the information available to direct models, e.g., using user profiles, endorsements, etc., with data from various other external knowledge bases to find implicit topics for users, and other types of reasoning to compute proficiency scores. Implicit topics, i.e., those topics related to explicitly identified areas of expertise, can be determined based on a topic graph such as a Directed Acyclic Graph (DAG). Generally speaking, embodiments can traverse the topic graph for explicitly identified skills or topics and determine related or similar new skills based on nearby nodes of the graph. This approach can uncover skills that user that may not have disclosed as well as scoring users on skills based on the skill's similarity to those the user did claim.