Understanding the structure of knowledge graphs with ABSTAT profiles

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by task of understanding content as result size complexity. Data profiling approaches have proposed to summarize large KGs into concise meaningful representations, so that they can be better explored, processed, managed. Profiles based on schema patterns represent each triple KG with its schema-level counterpart, thus covering entire profiles considerable size. In this paper, we provide empirical evidence patterns, if explored suitable mechanisms, useful help understand big complex KGs. ABSTAT provides pattern-based comes faceted interfaces profile exploration. Using tool present user study query completion tasks. We demonstrate who look at formulate queries faster than browsing ontology The latter is pretty strong baseline considering many do not even come specific users. To best our knowledge, first attempt investigate impact techniques tasks related knowledge graph study.