Instance-based probabilistic reasoning in the semantic web

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. Categories and Subject Descriptors I.2.3 [Artificial Intelligence]: Deduction and Theorem Proving – uncertainty, “fuzzy”, and probabilistic reasoning. General Terms Algorithms, Experimentation. Keywords Semantic Web, Probabilistic Reasoning, Markov Logic.

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Pedro Oliveira, Paulo Gomes
• Keywords: Internet Technology, Markov Logic, Probabilistic Reasoning, Semantic Web, WWW 2009