Rules capture high-level patterns in knowledge graphs and sometimes can provide virtual schemata for the data. Recent research has witnessed an increasing interest in learning rules automatically and applying the learned rules in knowledge graph inferences. While quite a number of rule learning systems have been developed, the formats of learned rules are still restricted, mostly resemble paths in the knowledge graphs. Such methods focus on the graph structure of the knowledge graph, assuming all the vertices and all the edges in the graph have an equal role (while only named or labelled differently). In this paper, we propose a method HARL (Hub Augmented Rule Learning) for learning rules containing attributes by treating certain binary predicates as unary predicates. A major component of HARL is an algorithm for identifying attributes for a given knowledge graph. HARL has been implemented through a scalable rule learner RLvLR. Our experimental results also show that HARL is scalable and outperforms RLvLR in most cases on major benchmark datasets.