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 December 13, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
A Probabilistic Framework for Semantic Similarity and Ontology Mapping
Published
Author(s)
, , , , , Rong Pan, Yang Yu,
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
Download Paper
Keywords
Bayesian networks, integration, ontology, Semantic web, uncertainty
Citation
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