Abstract
We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the considered similarities allow to separate true axioms from false axioms. The result of the dimensionality reduction process is subsequently fed to several learning algorithms, comparing the accuracy of all combinations of similarity, dimensionality reduction technique, and classification method. As a result, it is observed that it is not necessary to use sophisticated semantics-based similarity measures to obtain accurate predictions, and furthermore that classification performance only marginally depends on the choice of the learning method. Our results open the way to implementing efficient surrogate models for axiom scoring to speed up ontology learning and schema induction methods.
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Notes
- 1.
It should always be borne in mind that this is just a shorthand notation for the underlying OWL 2 functional-style syntax extended with the “minus” operator as explained above.
- 2.
We recall that \(N(\phi ) = 1 - \varPi (\lnot \phi )\) and \(\varPi (\phi ) = 1 - N(\lnot \phi )\).
- 3.
The used implementations of t-SNE and PCA in scikit-learn [16] accept, respectively, a distance and a similarity matrix. Thus we normalized all similarities.
- 4.
Code and data to replicate all experiments described in the paper is available at https://github.com/dariomalchiodi/MDAI2020.
- 5.
We also repeated all experiments considering both antecedents and consequents, obtaining comparable results.
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Acknowledgments
Part of this work was done while D. Malchiodi was visiting scientist at Inria Sophia-Antipolis/I3S CNRS Université Côte d’Azur. This work has been supported by the French government, through the 3IA Côte d’Azur “Investments in the Future” project of the Nat’l Research Agency, ref. no. ANR-19-P3IA-0002.
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Malchiodi, D., da Costa Pereira, C., Tettamanzi, A.G.B. (2020). Classifying Candidate Axioms via Dimensionality Reduction Techniques. In: Torra, V., Narukawa, Y., Nin, J., Agell, N. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2020. Lecture Notes in Computer Science(), vol 12256. Springer, Cham. https://doi.org/10.1007/978-3-030-57524-3_15
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