Scalable Prediction of Atomic Candidate OWL Class Axioms Using a Vector-Space Dimension Reduced Approach
Résumé
Scoring candidate axioms or assessing their acceptability against known evidence is essential for automated schema induction and can also be valuable for knowledge graph validation. However, traditional methods for accurately scoring candidate axioms are often computationally and storage expensive, making them impractical for use with large knowledge graphs. In this work, we propose a scalable method to predict the scores of atomic candidate OWL class axioms of different types. The method relies on a semantic similarity measure derived from the ontological distance between concepts in a subsumption hierarchy, as well as feature ranking and selection for vector-space dimension reduction. We train a machine learning model using our reduced vector-space, encode new candidates as a vector, and predict their scores. Extensive tests that cover a range of ontologies of various sizes and multiple parameters and settings are carried out to investigate the effectiveness and scalability of the method.
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