Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: The present teaching relates to associating data from different sources to generate a person-centric space. In one example, information related to a person is received. First data is obtained from a first source private to the person based on the information. One or more types of cross-linking keys are identified from the first data. Second data is obtained from at least one second source based on the one or more types of cross-linking keys. The first data is associated with at least a portion of the second data based, at least in part, on the one or more types of cross-linking keys to generate cross-linked data. The person-centric space is generated based on the cross-linked data.

Abstract: The present teaching relates to associating data from different sources to generate a person-centric space. In one example, information related to a person is received. First data is obtained from a first source private to the person based on the information. One or more types of cross-linking keys are identified from the first data. Second data is obtained from at least one second source based on the one or more types of cross-linking keys. The first data is associated with at least a portion of the second data based, at least in part, on the one or more types of cross-linking keys to generate cross-linked data. The person-centric space is generated based on the cross-linked data.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: The present teaching relates to associating data from different sources to generate a person-centric space. In one example, information related to a person is received. First data is obtained from a first source private to the person based on the information. One or more types of cross-linking keys are identified from the first data. Second data is obtained from at least one second source based on the one or more types of cross-linking keys. The first data is associated with at least a portion of the second data based, at least in part, on the one or more types of cross-linking keys to generate cross-linked data. The person-centric space is generated based on the cross-linked data.

Abstract: The present teaching relates to searching in a person-centric space. In one example, a request related to a person is received for searching data. An entity is identified from the request. First data is retrieved from a person-centric space based on the entity. One or more cross-linking keys associated with the entity and/or the first data are determined. Second data is retrieved from the person-centric space based on the one or more cross-linking keys. The first and second data are provided as a response to the request. The person-centric space is associated with the person and comprises the entity and the one or more linking keys.