Inference in probabilistic ontologies with attributive concept descriptions and nominals

作者: Rodrigo Bellizia Polastro , Fabio Gagliardi Cozman

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摘要: This paper proposes a probabilistic description logic that combines (i) constructs of the well-known ALC logic, (ii) assertions, and (iii) limited use nominals. We start with our recently proposed CRALC, where any ontology can be translated into relational Bayesian network partially specified probabilities. then add nominals to restrictions, while keeping CRALC's interpretation-based semantics. discuss clash between domain-based semantics for an queries, latter throughout. show how inference conducted in CRALC present examples real ontologies display level scalability proposals.

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