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Knowledge graphs (KGs) store highly heterogeneous information about the worldin the structure of a graph, and are useful for tasks such as questionanswering and reasoning. However, they often contain errors and are missinginformation. Vibrant research in KG refinement has worked to resolve theseissues, tailoring techniques to either detect specific types of errors orcomplete a KG.  In this work, we introduce a unified solution to KG characterization byformulating the problem as unsupervised KG summarization with a set ofinductive, soft rules, which describe what is normal in a KG, and thus can beused to identify what is abnormal, whether it be strange or missing. Unlikefirst-order logic rules, our rules are labeled, rooted graphs, i.e., patternsthat describe the expected neighborhood around a (seen or unseen) node, basedon its type, and information in the KG. Stepping away from the traditionalsupport/confidence-based rule mining techniques, we propose KGist, KnowledgeGraph Inductive SummarizaTion, which learns a summary of inductive rules thatbest compress the KG according to the Minimum Description Length principle---aformulation that we are the first to use in the context of KG rule mining. Weapply our rules to three large KGs (NELL, DBpedia, and Yago), and tasks such ascompression, various types of error detection, and identification of incompleteinformation. We show that KGist outperforms task-specific, supervised andunsupervised baselines in error detection and incompleteness identification,(identifying the location of up to 93% of missing entities---over 10% more thanbaselines), while also being efficient for large knowledge graphs.

What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization