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Abstract
This paper investigates learning methods where the target language is the recently proposed probabilistic description logiccr\(\mathcal{ALC}\). We start with an inductive logic programming algorithm that learns logical constructs; we then develop an algorithm that learns probabilistic constructs by searching for conditioning concepts, using examples given as interpretations. Issues on learning from entailments are also examined, and practical examples are discussed.
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Authors and Affiliations
Departamento de Informática Aplicada, Unirio, Av. Pasteur, 458, Rio de Janeiro, RJ, Brazil
Kate Revoredo
Escola Politécnica, Universidade de São Paulo, Av. Prof. Mello Morais 2231, São Paulo, SP, Brazil
José Eduardo Ochoa-Luna & Fabio Gagliardi Cozman
- Kate Revoredo
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- José Eduardo Ochoa-Luna
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- Fabio Gagliardi Cozman
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Editors and Affiliations
FURG, Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Av. Itália, km 8 – Campus Carreiros, 96.201-900, Rio Grande, RS, Brazil
Antônio Carlos da Rocha Costa
UFRGS, Instituto de Informática, Universidade Federal do Rio Grande do Sul, Av. Bento Conçalves 9.500, 91501-970, Porto Alegre, RS, Brazil
Rosa Maria Vicari
Departamento de Ciência da Computação, Centro Universitário da FEI, Av. Humberto A. C. Branco 3972, 09850-901, São Bernardo do Campo, SP, Brazil
Flavio Tonidandel
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Revoredo, K., Ochoa-Luna, J.E., Cozman, F.G. (2010). Learning Terminologies in Probabilistic Description Logics. In: da Rocha Costa, A.C., Vicari, R.M., Tonidandel, F. (eds) Advances in Artificial Intelligence – SBIA 2010. SBIA 2010. Lecture Notes in Computer Science(), vol 6404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16138-4_5
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