A New Algorithm for Generating Situation-Specific Bayesian Networks Using Bayes-Ball Method

Multi-Entity Bayesian Network (MEBN) is an expressive first-order probabilistic logic that represents the domain using parameterized fragments of Bayesian networks. Probabilistic-OWL (PR-OWL) uses MEBN to add uncertainty support to OWL, the main language of the Semantic Web. The reasoning in MEBN is made by the construction of a Situation-Specific Bayesian Network (SSBN), a minimal Bayesian network sufficient to compute the response to queries. A Bottom-Up algorithm has been proposed for generating SSBNs in MEBN. However, this approach presents scalability problems since the algorithm starts from all the query and evidence nodes, which can be a very large set in real domains. To address this problem, we present a new scalable algorithm for generating SSBNs based on the Bayes-Ball method, a well-known and efficient algorithm for discovering d-separated nodes of target sets in Bayesian networks. The novel SSBN algorithm used together with Resource Description Framework (RDF) databases and PR-OWL 2 RL, an amenable version of PR-OWL, allows reasoning with probabilistic ontologies containing large assertive bases, offering a scalable approach for the treatment of uncertainty in the Semantic Web.