A Probabilistic Framework for Semantic Similarity and Ontology Mapping
Boonserm Kulvatunyou, Nenad Ivezic, Albert W. Jones, Yun Peng, Zhongli Ding, Rong Pan, Yang Yu, Hyunbo Cho
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 of the 2007 Industrial Engineering Research Conference
, Ivezic, N.
, Jones, A.
, Peng, Y.
, Ding, Z.
, Pan, R.
, Yu, Y.
and Cho, H.
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 9, 2023)