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 OWL 5 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 web 6 and discusses uncertainty in semantic web ontologies; Sect. 2

Semantic interoperability is the faculty of interpreting knowledge imported from other languages at the semantic level, i.e. to ascribe to each imported piece of knowledge the correct interpretation or set of models. It is a very important requirement for delivering a worldwide semantic web. This paper presents preliminary investigations towards developing a unified view of the problem. It proposes a definition of semantic interoperability based on model theory and shows how it applies to already existing works in the domain. Then, new applications of this definition to family of languages, ontology patterns and explicit description of semantics are presented.

Mediators are middleware components that provide a exible integration of several heterogeneous information systems. But current approaches ignore that each information has to be considered in its context. Besides the integration the information has to be also converted from its source context into the context of the integrated view in order to achieve interoperability at the semantic level. Two dierent methods for context transformation are discussed: a rule{based and a classication{based context transformation. We argue that the rule-based context transformation approach does only provide solutions for rather simple transformation tasks. We present an alternative approach that is based on sophisticated context modeling using ontologies based on description logics. We show that this approach supports context transformation based on subsumption reasoning oering reasonable results. Both methods are explained with the help of a real world example coming from the geographic...

The development towards open geographical information systems (GIS) and the interoperability between these systems demand new requirements for the description of the underlying data. The exchange of data between GIS is problematic and often fails due to confusion in the meaning of concepts. The term semantic translator, a translator between GIS and/or catalogue systems which gives the user the option to map data between the systems is a current research topic. This paper proposes an overview of formal ontologies and how they can be used for geographical information integration. A description of an intelligent architecture for semantic-based information retrieval is introduced and shows in a case study how this approach can be used for general purposes.

Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Semantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies. Degrees of overlap indicate how well an individual data item matches the query concept, which can be used as a well-defined measure of relevance in information retrieval tasks.

this report is to bring together two current research activities, carried out at the Vrije Universiteit Amsterdam and at the Center for Computing Technologies at the University of Bremen. The context of the research at the Free University is the European project "On-To-Knowledge" #www.ontoknowledge.com# whose goal it is to provide support for e#cient and e#ective knowledge management. The scope of the project are weakly structured information sources in the internet. The approach of the project is to use ontologies to provide explicitly available semantic information about information sources in order to support the acquisition, maintenance and access to these information sources. Background of the research at the Center for Computing Technologies is the Project INTEREC-IIC Project "DataShare" #Voegele, 2000#. The goal of this project is to provide intelligent support for processes concerned with environmental protection. Scope of the work at the center for computing technologies is a common access to available information sources throughout the whole process. The main problem occurring in this context is the heterogeneity of the information needed in a process

This paper discusses the use of contextual reasoning, i.e.

Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Semantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies. Degrees of overlap indicate how well an individual data item matches the query concept, which can be used as a well-defined measure of relevance in information retrieval tasks.

Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Semantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies. Degrees of overlap indicate how well an individual data item matches the query concept, which can be used as a well-defined measure of relevance in information retrieval tasks.

Exchanging knowledge via the web might lead to the use of different representation languages because different applications could take advantage of this knowledge. In order to function properly, the interoperability of these languages must be established on a semantic ground (i.e., based on the models of the representations). Several solutions can be used for ensuring this interoperability.