Abstract

Knowledge graphs (KGs) represent facts in the form of nodes and relationships and are widely used to represent and share knowledge in many different domains. However, their widespread adoption to integrate different data sources and their generation processes have made KGs very complicated and difficult to understand, leading to the advent of new knowledge graph exploration approaches to better understand their contents and extract relevant insights. Nevertheless, the needs of current KG exploration use cases are not met (even neglected) by existing KG data management systems. Hence, the question: are we lost? We hope not. Therefore, with the aim of fostering research on these open issues, in this position paper, we first present an overview of state-of-the-art approaches for KG exploration. Then, we identify the (currently unmet) requirements for effective KG exploration systems, and finally, we highlight promising research directions for the realization of a system able to fully support knowledge graph exploration.