A Probabilistic Framework for Semantic Similarity and Ontology Mapping

Published

April 1, 2007

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

Pub Type

Conferences

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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 October 6, 2025)