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Mining Bayesian networks out of ontologies

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Abstract

Probabilistic reasoning is an essential feature when dealing with many application domains. Starting with the idea that ontologies are the right way to formalize domain knowledge and that Bayesian networks are the right tool for probabilistic reasoning, we propose an approach for extracting a Bayesian network from a populated ontology and for reasoning over it. The paper presents the theory behind the approach, its design and examples of its use.

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Notes

  1. Please refer to http://www.w3.org/TR/rdf-mt/ for OWL definitions and notions related to RDF.

  2. Note that the concept of “belonging” referred to each instance can be thought of as “involved in is-a relation”. P(a) means that a is involved in is-a relation, because each ontology instance always belongs to some class. P(a|b) means that an is-a relation exists between a and b.

  3. We do not consider constraints on logical relations among classes such as intersection, disjoint, union, and so on.

  4. The rules for reasoning at the lower level are structurally the same, although arcs at the lower level represent is-a relationships.

  5. The computation process of the same factors with \(\overline{Q}\) instead of Q, is analogous and is omitted.

  6. When T i is not a root node, the prior probability is computed by summing the prior probability of the parent nodes recursively, until the root nodes are reached.

  7. The computation process of the same factors with \(\overline{COMPANY}\) instead of COMPANY, is analogous and is omitted.

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Authors and Affiliations

  1. Livorno Port Authority, Livorno, Italy

    Bellandi Andrea

  2. Department of Computer Science, University of Pisa, Pisa, Italy

    Turini Franco

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  1. Bellandi Andrea
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  2. Turini Franco
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Correspondence to Bellandi Andrea.

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Andrea, B., Franco, T. Mining Bayesian networks out of ontologies. J Intell Inf Syst 38, 507–532 (2012). https://doi.org/10.1007/s10844-011-0165-4

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  • DOI: https://doi.org/10.1007/s10844-011-0165-4

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