Abstract
We present a method for boosting relational classifiers of individual resources in the context of the Web of Data. We show how weak classifiers induced by simple concept learners can be enhanced producing strong classification models from training datasets. Even more so the comprehensibility of the model is to some extent preserved as it can be regarded as a sort of concept in disjunctive form. We demonstrate the application of this approach to a weak learner that is easily derived from learners that search a space of hypotheses, requiring an adaptation of the underlying heuristics to take into account weighted training examples. An experimental evaluation on a variety of artificial learning problems and datasets shows that the proposed approach enhances the performance of the basic learners and is competitive, outperforming current concept learning systems.
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Notes
- 1.
The source code, the datasets, the ontologies and supplemental material are publicly available at: https://bitbucket.org/grizzo001/DLbooster/src/master/.
- 2.
JFact was used: http://jfact.sourceforge.net.
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Fanizzi, N., Rizzo, G., d’Amato, C. (2019). Boosting DL Concept Learners. In: Hitzler, P., et al. The Semantic Web. ESWC 2019. Lecture Notes in Computer Science(), vol 11503. Springer, Cham. https://doi.org/10.1007/978-3-030-21348-0_5
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