Social networks today represent a substantial amount of shared knowledge and information. To leverage the interdependence of this data, we consider two forms of relational learning to facilitate semantic understanding. First, relational modeling is applied to local networks to reinforce knowledge in each entity. Then, a social dimension approach is applied to generate new (high level) features. These feature sets are then trained towards the identification of learned purchase behaviors (belief system / values) thus supporting a means of prediction. We consider this generation of higher level classifications (termed as social dimensions) to enable increased accuracy in behavior prediction in order to support more focused customer relationships.