Updating RDF in the semantic web

RDF is widely used in the Semantic Web for representing ontology data. Many real world RDF collections are large and contain complex graph relationships that represent knowledge in a particular domain. Such large RDF collections evolve as a consequence of their representation of the changing world. Evolution in Semantic Web content produces difference files (deltas) that track changes between ontology versions. These changes may represent ontology modifications or simply changes in application data. An ontology is typically expressed in a combination of OWL, RDFS and RDF knowledge representation languages. A data repository that represents an ontology may be large and may be duplicated over the Internet, often in the form of a relational data store. Although this data may be distributed over the Internet, it needs to be managed and updated in the face of such evolutionary changes. In view of the size of typical collections, it is important to derive efficient ways of propagating updates to distributed datastores. The deltas can be used to reduce the storage and bandwidth overhead involved in disseminating ontology updates. Minimising the delta size can be achieved by reasoning over the underlying knowledge base. OWL 2 is a development of the OWL 1 standard that incorporate new features to aid application construction. Among the sub languages of OWL 2, OWL 2 RL/RDF provides an enriched rule set that extends the semantic capability of the OWL environment. This additional semantic content can be exploited in change detection approaches that strive to minimise the alterations to be made when ontologies are updated. The presence of blank nodes (i.e. nodes that are neither a URI nor a literal) in RDF collections provides a further challenge to ontology change detection. This is a consequence of the practical problems they introduce when comparing data structures before and after an update.The contribution of this thesis is a detailed analysis of the performance of RDF change detection techniques. In addition, the work proposes a new approach to maintaining the consistency of RDF by using knowledge embedded in the structure to generate efficient update transactions. The evaluation of this approach indicates that it reduces the overall update size, at the cost of increasing the processing time needed to generate the transactions.In the light of OWL 2 RL/RDF, this thesis examines the potential for reducing the delta size by pruning the application of unnecessary rules from the reasoning process and using an approach to delta generation that produces a small number of updates. It also assesses the impact of alternative approaches to handling blank nodes during the change detection process in ontology structures. The results indicate that pruning the rule set is a potentially expensive process but has the benefit of reducing the joins over relational data stores when carrying out the subsequent inferencing.RDF is widely used in the Semantic Web for representing ontology data. Many real world RDF collections are large and contain complex graph relationships that represent knowledge in a particular domain. Such large RDF collections evolve as a consequence of their representation of the changing world. Evolution in Semantic Web content produces difference files (deltas) that track changes between ontology versions. These changes may represent ontology modifications or simply changes in application data. An ontology is typically expressed in a combination of OWL, RDFS and RDF knowledge representation languages. A data repository that represents an ontology may be large and may be duplicated over the Internet, often in the form of a relational data store. Although this data may be distributed over the Internet, it needs to be managed and updated in the face of such evolutionary changes. In view of the size of typical collections, it is important to derive efficient ways of propagating updates to distributed datastores. The deltas can be used to reduce the storage and bandwidth overhead involved in disseminating ontology updates. Minimising the delta size can be achieved by reasoning over the underlying knowledge base. OWL 2 is a development of the OWL 1 standard that incorporate new features to aid application construction. Among the sub languages of OWL 2, OWL 2 RL/RDF provides an enriched rule set that extends the semantic capability of the OWL environment. This additional semantic content can be exploited in change detection approaches that strive to minimise the alterations to be made when ontologies are updated. The presence of blank nodes (i.e. nodes that are neither a URI nor a literal) in RDF collections provides a further challenge to ontology change detection. This is a consequence of the practical problems they introduce when comparing data structures before and after an update.The contribution of this thesis is a detailed analysis of the performance of RDF change detection techniques. In addition, the work proposes a new approach to maintaining the consistency of RDF by using knowledge embedded in the structure to generate efficient update transactions. The evaluation of this approach indicates that it reduces the overall update size, at the cost of increasing the processing time needed to generate the transactions.In the light of OWL 2 RL/RDF, this thesis examines the potential for reducing the delta size by pruning the application of unnecessary rules from the reasoning process and using an approach to delta generation that produces a small number of updates. It also assesses the impact of alternative approaches to handling blank nodes during the change detection process in ontology structures. The results indicate that pruning the rule set is a potentially expensive process but has the benefit of reducing the joins over relational data stores when carrying out the subsequent inferencing