Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study
This addresses scalability problems for researchers mining patterns in large knowledge graphs, but it is a preliminary study.
The paper tackles scalability issues in mining path patterns from knowledge graphs by proposing a constraint-based approach using monotonicity, and illustrates it on a biomedical knowledge graph (PGxLOD).
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.