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An algorithm based on counterfactuals for concept learning in the Semantic Web

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Abstract

In the line of realizing the Semantic-Web by means of mechanized practices, we tackle the problem of building ontologies, assisting the knowledge engineers’ job by means of Machine Learning techniques. In particular, we investigate on solutions for the induction of concept descriptions in a semi-automatic fashion. In particular, we present an algorithm that is able to infer definitions in the \(\mathcal{ALC}\) Description Logic (a sub-language of OWL-DL) from instances made available by domain experts. The effectiveness of the method with respect to past algorithms is also empirically evaluated with an experimentation in the document image understanding domain.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona 4, 70125, Bari, Italy

    Luigi Iannone, Ignazio Palmisano & Nicola Fanizzi

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  1. Luigi Iannone
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  2. Ignazio Palmisano
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  3. Nicola Fanizzi
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Correspondence to Luigi Iannone.

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Iannone, L., Palmisano, I. & Fanizzi, N. An algorithm based on counterfactuals for concept learning in the Semantic Web. Appl Intell 26, 139–159 (2007). https://doi.org/10.1007/s10489-006-0011-5

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