  Features mined from knowledge graphs are widely used within multiple
knowledge discovery tasks such as classification or fact-checking. Here, we
consider a given set of vertices, called seed vertices, and focus on mining
their associated neighboring vertices, paths, and, more generally, path
patterns that involve classes of ontologies linked with knowledge graphs. Due
to the combinatorial nature and the increasing size of real-world knowledge
graphs, the task of mining these patterns immediately entails scalability
issues. In this paper, we address these issues by proposing a pattern mining
approach that relies on a set of constraints (e.g., support or degree
thresholds) and the monotonicity property. As our motivation comes from the
mining of real-world knowledge graphs, we illustrate our approach with PGxLOD,
a biomedical knowledge graph.
