Matters

UMBC eBiquity Publication: A Bayesian Methodology towards Automatic Ontology Mapping: "Abstract: This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we devel-oped for modeling uncertainty in semantic web. The pro-posed method includes four components: 1) learning prob-abilities (priors about concepts, conditionals between sub-concepts and superconcepts, and raw semantic similarities between concepts in two different ontologies) using Naive Bayes text classification technique, by explicitly associating a concept with a group of sample documents retrieved and selected automatically from World Wide Web (WWW); 2) representing in OWL the learned probability information concerning the entities and relations in given ontologies; 3) using the BayesOWL framework to automatically translate given ontologies into the Bayesian network (BN) structures and to construct the conditional probability tables (CPTs) of a BN from those learned priors or conditionals, with reason-ing services within a single ontology supported by Bayesian inference; and 4) taking a set of learned initial raw similari-ties as input and finding new mappings between concepts from two different ontologies as an application of our for-malized BN mapping theory that is based on evidential rea-soning across two BNs. "