  Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual
information about real-world entities. Understanding the uniqueness of each
entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional
profiling technologies encompass a vast array of methods to find distinctive
features in various applications, which can help to differentiate entities in
the process of human understanding of KGs. In this work, we present a novel
profiling approach to identify distinctive entity features. The distinctiveness
of features is carefully measured by a HAS model, which is a scalable
representation learning model to produce a multi-pattern entity embedding. We
fully evaluate the quality of entity profiles generated from real KGs. The
results show that our approach facilitates human understanding of entities in
KGs.
