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

Pierre Monnin 1 Emmanuel Bresso 2 Miguel Couceiro 1 Malika Smaïl-Tabbone 1 Amedeo Napoli 1 Adrien Coulet 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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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Contributor : Pierre Monnin <>
Submitted on : Friday, August 7, 2020 - 6:37:01 PM
Last modification on : Tuesday, March 9, 2021 - 11:33:44 AM
Long-term archiving on: : Monday, November 30, 2020 - 4:32:01 PM


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  • HAL Id : hal-02913224, version 1
  • ARXIV : 2007.08821


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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