Soft Computing in Ontologies and Semantic Web
This book covers in a great depth the fast growing topic of tools, techniques and applications of soft computing (e.g., fuzzy logic, genetic algorithms, neural networks, rough sets, Bayesian networks, and other probabilistic techniques) in the ontologies
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Probability in Ontologies and Semantic Web
BayesOWL: Uncertainty Modeling in Semantic Web Ontologies Zhongli Ding1,2 , Yun Peng1,3 , and Rong Pan1,4 1 2 3 4
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland 21250, USA [email protected] [email protected] [email protected]
It is always essential but difficult to capture incomplete, partial or uncertain knowledge when using ontologies to conceptualize an application domain or to achieve semantic interoperability among heterogeneous systems. This chapter presents an on-going research on developing a framework which augments and supplements the semantic web ontology language OWL5 for representing and reasoning with uncertainty based on Bayesian networks (BN) [26], and its application in ontology mapping. This framework, named BayesOWL, has gone through several iterations since its conception in 2003 [8, 9]. BayesOWL provides a set of rules and procedures for direct translation of an OWL ontology into a BN directed acyclic graph (DAG), it also provides a method based on iterative proportional fitting procedure (IPFP) [19, 7, 6, 34, 2, 4] that incorporates available probability constraints when constructing the conditional probability tables (CPTs) of the BN. The translated BN, which preserves the semantics of the original ontology and is consistent with all the given probability constraints, can support ontology reasoning, both within and across ontologies as Bayesian inferences. At the present time, BayesOWL is restricted to translating only OWL-DL concept taxonomies into BNs, we are actively working on extending the framework to OWL ontologies with property restrictions. If ontologies are translated to BNs, then concept mapping between ontologies can be accomplished by evidential reasoning across the translated BNs. This approach to ontology mapping is seen to be advantageous to many existing methods in handling uncertainty in the mapping. Our preliminary work on this issue is presented at the end of this chapter. This chapter is organized as follows: Sect. 1 provides a brief introduction to semantic web6 and discusses uncertainty in semantic web ontologies; Sect. 2 5 6
http://www.w3.org/2001/sw/WebOnt/ http://www.w3.org/DesignIssues/Semantic.html
Z. Ding et al.: BayesOWL: Uncertainty Modeling in Semantic Web Ontologies, StudFuzz 204, 3–29 (2006) c Springer-Verlag Berlin Heidelberg 2006 www.springerlink.com
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describes BayesOWL in detail; Sect. 3 proposes a representation in OWL of probability information concerning the entities and relations in ontologies; and Sect. 4 outlines how BayesOWL can be applied to automatic ontology mapping. The chapter ends with a discussion and suggestions for future research in Sect. 5.
1 Semantic Web, Ontology, and Uncertainty People can read and understand a web page easily, but machines can not. To make web pages understandable by machines, additional semantic information needs to be attached or embedded to the existing web data. Built upon
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