A Bayesian Approach to Uncertainty Modelling in OWL Ontology

Abstract

Dealing with uncertainty is crucial in ontology engineering tasks such as domain modelling, ontology reasoning, and concept mapping between ontologies. This paper presents the authors' on-going research on modelling uncertainty in ontologies based on Bayesian networks (BN). The work includes the following: (1) extending OWL to allow additional probabilistic markups for attaching probability information, (2) directly converting a probabilistically annotated OWL ontology into a BN structure by a set of structural translation rules, and (3) constructing the conditional probability tables (CPTs) of this BN using a new method based on iterative proportional fitting procedure (IPFP). The translated BN can support more accurate ontology reasoning under uncertainty as Bayesian inferences.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA444453

Entities

People

  • Rong Pan
  • Yun Peng
  • Zhongli Ding

Organizations

  • University of Maryland, Baltimore

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computer Science
  • Electrical Engineering
  • Electronic Mail
  • Engineering
  • Models
  • Ontologies
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Reasoning
  • Semantics
  • Taxonomy
  • Translations
  • Uncertainty

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval
  • AI & ML - Machine Translation