BayesOWL: Uncertainty Modeling in Semantic Web Ontologies [chapter]

Zhongli Ding, Yun Peng, Rong Pan
Soft Computing in Ontologies and Semantic Web  
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 OWL 5 for representing and reasoning with uncertainty based on Bayesian networks (BN) [22] , and its application in the field of ontology mapping. This framework, named BayesOWL
more » ... rk, named BayesOWL [7, 8], provides a set of rules and procedures for direct translation of an OWL ontology into a BN directed acyclic graph (DAG) and a method based on iterative proportional fitting procedure (IPFP) [17, 6, 5, 29, 1, 3] 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. A representation in OWL of probability information concerning the entities and relations in ontologies is also proposed. 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 web 6 , what the term "ontology" means, and the necessity to be able to do reasoning on partial or noisy input in a disciplined manner; Sect. 2 describes BayesOWL in detail; Sect. 3 proposes a representation of probability in OWL; and Sect. 4 focuses on how to apply BayesOWL for automatic 5
doi:10.1007/978-3-540-33473-6_1 fatcat:bvzzssgtprhojpmxr5nymhtaxy