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Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study

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Pierre Monnin
Miguel Couceiro
Amedeo Napoli
Adrien Coulet

Abstract

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.
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Dates and versions

hal-02913224 , version 1 (07-08-2020)

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Pierre Monnin, Emmanuel Bresso, Miguel Couceiro, Malika Smaïl-Tabbone, Amedeo Napoli, et al.. Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study. ALGOS 2020 - 1st International Conference on Algebras, Graphs and Ordered Sets, Aug 2020, Nancy, France. ⟨hal-02913224⟩
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