NIST logo

Publication Citation: A Probabilistic Framework for Semantic Similarity and Ontology Mapping

NIST Authors in Bold

Author(s): Boonserm Kulvatunyou; Nenad Ivezic; Albert W. Jones; Yun Peng; Zhongli Ding; Rong Pan; Yang Yu; Hyunbo Cho;
Title: A Probabilistic Framework for Semantic Similarity and Ontology Mapping
Published: April 01, 2007
Abstract: We propose a probabilistic framework to address uncertainty in ontology-based semantic integration and interopera-tion. This framework consists of three main components: 1) BayesOWL that translates an OWL ontology to a Bayes-ian network, 2) SLBN (Semantically Linked Bayesian Networks) that support reasoning across translated BNs, and 3) a Learner that learns from the web the probabilities needed by the other modules. This framework expands the semantic web and can serve as a theoretical basis for solving real world semantic integration problems.
Proceedings: Proceedings of the 2007 Industrial Engineering Research Conference
Location: Orlando, FL
Dates: May 7-12, 2007
Keywords: Bayesian networks;integration;ontology;Semantic web;uncertainty
Research Areas: Ontologies, Manufacturing
PDF version: PDF Document Click here to retrieve PDF version of paper (153KB)