Instance-based probabilistic reasoning in the semantic web

ABSTRACT

Most of the approaches for dealing with uncertainty in the Semantic Web rely on the principle that this uncertainty is already asserted. In this paper, we propose a new approach to learn and reason about uncertainty in the Semantic Web. Using instance data, we learn the uncertainty of an OWL ontology, and use that information to perform probabilistic reasoning on it. For this purpose, we use Markov logic, a new representation formalism that combines logic with probabilistic graphical models.

REFERENCES

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• 2: Thomas Lukasiewicz , Umberto Straccia, Managing uncertainty and vagueness in description logics for the Semantic Web, Web Semantics: Science, Services and Agents on the World Wide Web, v.6 n.4, p.291-308, November, 2008 [doi>10.1016/j.websem.2008.04.001]
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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

General Terms:
Algorithms, Experimentation

Keywords:
markov logic, probabilistic reasoning, semantic web