Large-scale knowledge graphs (KGs) contain massive entities and abundant relations among the entities. Data exploration over KGs allows users to browse the attributes of entities as well as the relations among entities. It therefore provides a good way of learning the structure and coverage of KGs. In this paper, we introduce a system called SEED that is designed to support entity-oriented exploration in large-scale KGs, based on retrieving similar entities of some seed entities as well as their semantic relations that show how entities are similar to each other. A by-product of entity exploration in SEED is to facilitate discovering the deficiency of KGs, so that the detected bugs can be easily fixed by users as they explore the KGs.