Zhongli Ding

BayesOWL: Uncertainty Modeling in Semantic Web Ontologies

It is always essential but di±cult 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 OWL for representing and reasoning with uncertainty based on Bayesian networks (BN), and its application in ontology mapping.

Using Ontologies in the Semantic Web: A Survey

The Semantic Web is well recognized as an effective infrastructure to enhance visibility of knowledge on the Web. The core of the Semantic Web is “ontology”, which is used to explicitly represent our conceptualizations. Ontology engineering in the Semantic Web is primarily supported by languages such as RDF, RDFS and OWL. This chapter discusses the requirements of ontology in the context of the Web, compares the above three languages with existing knowledge representation formalisms, and surveys tools for managing and applying ontology. Advantages of using ontology in both knowledge-base-style and database-style applications are demonstrated using three real world applications.

Belief Update in Bayesian Networks Using Uncertain Evidence

This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffreys rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods. This in-depth understanding leads us to propose two algorithms for belief update with multiple soft evidences. Both of these algorithms can be seen as integrating the techniques of virtual evidence method, IPFP and traditional BN evidential inference, and they have clear computational and practical advantages over the methods proposed by others in the past

BAYESIAN NETWORK REVISION WITH PROBABILISTIC CONSTRAINTS

This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability distribution of the revised BN is as close as possible to that of the original BN. Two variations of E-IPFP are also proposed: 1) E-IPFP-SMOOTH which deals with the situation where the probabilistic constraints are inconsistent with each other or with the network structure of the given BN; and 2) D-IPFP which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems.