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A Probabilistic Framework for Semantic Similarity and Ontology Mapping

Published

Author(s)

Boonserm Kulvatunyou, Nenad Ivezic, Albert W. Jones, Yun Peng, Zhongli Ding, Rong Pan, Yang Yu, Hyunbo Cho

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 Title
Proceedings of the 2007 Industrial Engineering Research Conference
Conference Dates
May 7-12, 2007
Conference Location
Orlando, FL

Keywords

Bayesian networks, integration, ontology, Semantic web, uncertainty

Citation

Kulvatunyou, B. , Ivezic, N. , Jones, A. , Peng, Y. , Ding, Z. , Pan, R. , Yu, Y. and Cho, H. (2007), A Probabilistic Framework for Semantic Similarity and Ontology Mapping, Proceedings of the 2007 Industrial Engineering Research Conference, Orlando, FL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822710 (Accessed June 19, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created April 1, 2007, Updated February 19, 2017