Skip to Main content Skip to Navigation
Conference papers

Classifying Candidate Axioms via Dimensionality Reduction Techniques

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [27 references]  Display  Hide  Download

https://hal.inria.fr/hal-02931396
Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Monday, September 7, 2020 - 10:39:30 AM
Last modification on : Thursday, January 21, 2021 - 2:32:02 PM
Long-term archiving on: : Friday, December 4, 2020 - 5:45:04 PM

File

mdai-2020-camera-ready.pdf
Files produced by the author(s)

Identifiers

Citation

Dario Malchiodi, Célia da Costa Pereira, Andrea G. B. Tettamanzi. Classifying Candidate Axioms via Dimensionality Reduction Techniques. MDAI 2020 - 17th International Conference on Modeling Decisions for Artificial Intelligence, Sep 2020, Sant Cugat, Spain. pp.179-191, ⟨10.1007/978-3-030-57524-3_15⟩. ⟨hal-02931396⟩

Share

Metrics

Record views

51

Files downloads

334