Title :
OntoBayes: An Ontology-Driven Uncertainty Model
Author :
Yang, Yi ; Calmet, Jacques
Author_Institution :
Inst. for Algorithms & Cognitive Syst., Karlsruhe Univ.
Abstract :
This paper describes an ontology-driven model, which integrates Bayesian networks (BN) into the Ontology Web Language (OWL) to preserve the advantages of both. This model makes use of probability and dependency-annotated OWL to represent uncertain information in BN structures. These extensions enhance knowledge representation in OWL and enable agents to act under uncertainty and complex structured open environments at the same time. This paper presents the underlying principles and scratches the surface of the decision theoretic agent system design based on "OntoBayes"
Keywords :
belief networks; decision theory; knowledge representation languages; ontologies (artificial intelligence); probability; software agents; uncertainty handling; Bayesian network; OntoBayes; Ontology Web Language; complex structured open environment; decision theoretic agent system design; dependency-annotated OWL; knowledge representation; ontology-driven uncertainty model; probability; Bayesian methods; Frequency; Intelligent agent; Intelligent systems; Knowledge representation; Machine intelligence; OWL; Ontologies; Semantic Web; Uncertainty;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
DOI :
10.1109/CIMCA.2005.1631307
