Semantic knowledge extraction from relational databases

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dc.contributor.advisor Fonou Dombeu, Jean Vincent Mogotlane, Kgotatso Desmond 2017-05-09T00:36:50Z 2017-05-09T00:36:50Z 2014-05
dc.description M. Tech. (Information Technology, Department of Information and Communications Technology, Faculty of Applied an Computer Sciences), Vaal University of Technolog en_US
dc.description.abstract One of the main research topics in Semantic Web is the semantic extraction of knowledge stored in relational databases through ontologies. This is because ontologies are core components of the Semantic Web. Therefore, several tools, algorithms and frameworks are being developed to enable the automatic conversion of relational databases into ontologies. Ontologies produced with these tools, algorithms and frameworks needs to be valid and competent for them to be useful in Semantic Web applications within the target knowledge domains. However, the main challenges are that many existing automatic ontology construction tools, algorithms, and frameworks fail to address the issue of ontology verification and ontology competency evaluation. This study investigates possible solutions to these challenges. The study began with a literature review in the semantic web field. The review let to the conceptualisation of a framework for semantic knowledge extraction to deal with the abovementioned challenges. The proposed framework had to be evaluated in a real life knowledge domain. Therefore, a knowledge domain was chosen as a case study. The data was collected and the business rules of the domain analysed to develop a relational data model. The data model was further implemented into a test relational database using Oracle RDBMS. Thereafter, Protégé plugins were applied to automatically construct ontologies from the relational database. The resulting ontologies are further validated to match their structures against existing conceptual database-to-ontology mapping principles. The matching results show the performance and accuracy of Protégé plugins in automatically converting relational databases into ontologies. Finally, the study evaluated the resulting ontologies against the requirements of the knowledge domain. The requirements of the domain are modelled with competency questions (CQs) and mapped to the ontology using SPARQL queries design, execution and analysis against users’ views of CQs answers. Experiments show that, although users have different views of the answers to CQs, the execution of the SPARQL translations of CQs against the ontology does produce outputs instances that satisfy users’ expectations. This indicates that Protégé plugins generated ontology from relational database embodies domain and semantic features to be useful in Semantic Web applications. en_US
dc.format.extent x, 71 leaves: illustrations, diagrams en_US
dc.language.iso en en_US
dc.subject Semantic web en_US
dc.subject Ontologies en_US
dc.subject Ontology verification en_US
dc.subject Ontology competency evalutation en_US
dc.subject Knowledge domain en_US
dc.subject Relational database en_US
dc.subject Database-to-ontology mapping principles en_US
dc.subject Protégé en_US
dc.subject Oracle RDBMS en_US
dc.subject SPARQL en_US
dc.subject.ddc 005.7 en_US
dc.subject.lcsh Semantic web en_US
dc.subject.lcsh Ontologies (Information retrieval) en_US
dc.title Semantic knowledge extraction from relational databases en_US
dc.type Thesis en_US

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