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Nowadays, increasingly more data are available as knowledge graphs (KGs).While this data model supports advanced reasoning and querying, they remaindifficult to mine due to their size and complexity. Graph mining approaches canbe used to extract patterns from KGs. However this presents two main issues.First, graph mining approaches tend to extract too many patterns for a humananalyst to interpret (pattern explosion). Second, real-life KGs tend to differfrom the graphs usually treated in graph mining: they are multigraphs, theirvertex degrees tend to follow a power-law, and the way in which they modelknowledge can produce spurious patterns. Recently, a graph mining approachnamed GraphMDL+ has been proposed to tackle the problem of pattern explosion,using the Minimum Description Length (MDL) principle. However, GraphMDL+, likeother graph mining approaches, is not suited for KGs without adaptations. Inthis paper we propose KG-MDL, a graph pattern mining approach based on the MDLprinciple that, given a KG, generates a human-sized and descriptive set ofgraph patterns, and so in a parameter-less and anytime way. We report onexperiments on medium-sized KGs showing that our approach generates sets ofpatterns that are both small enough to be interpreted by humans and descriptiveof the KG. We show that the extracted patterns highlight relevantcharacteristics of the data: both of the schema used to create the data, and ofthe concrete facts it contains. We also discuss the issues related to mininggraph patterns on knowledge graphs, as opposed to other types of graph data.

KG-MDL: Mining Graph Patterns in Knowledge Graphs with the MDL Principle