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The World Wide Web enables an easy, instant access to a huge quantity of information. Over the last few decades, a number of improvements have been achieved that helped the web reach its current state. However, the current Internet links documents together without understanding them, and thus, makes the content of web only human-readable rather than machine-understandable. Therefore, there is a growing need for an efficient web to make information machine understandable rather than only machine processable to reach to the web of knowledge. To cure this problem, the Semantic Web or what is called "web of meaning" tries to shift the thinking of published data in the form of web pages to allow machines to understand the contents. That is, computers are able to interoperate and think on our behalf, opening up several different perspectives. However, with the increasing quantity of semantic data, there is a need for efficient and scalable performance from semantic repositories which store and from which must be retrieving a large datasets contain Resource Description Framework--RDF--triples. This is a major obstacle to reaching the goal of the Semantic Web, and this problem is magnified by the unpredictable nature of the data encoded in RDF. Additionally, current RDF stores, in general, scale poorly, which may exacerbate the performance behavior for querying and retrieving RDF triples. As a consequence, we proposed new semantic storage models for managing RDF data in relational databases to show how a state-of-the-art scaling method can be improved with ontology-based techniques for speed and high scalability. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The World Wide Web enables an easy, instant access to a huge quantity of information. Over the last few decades, a number of improvements have been achieved that helped the web reach its current state. However, the current Internet links documents together without understanding them, and thus, makes the content of web only human-readable rather than machine-understandable. Therefore, there is a growing need for an efficient web to make information machine understandable rather than only machine processable to reach to the web of knowledge. To cure this problem, the Semantic Web or what is called “web of meaning” tries to shift the thinking of published data in the form of web pages to allow machines to understand the contents. That is, computers are able to interoperate and think on our behalf, opening up several different perspectives. However, with the increasing quantity of semantic data, there is a need for efficient and scalable performance from semantic repositories which store and from which must be retrieving a large datasets contain Resource Description Framework -RDF- triples. This is a major obstacle to reaching the goal of the Semantic Web, and this problem is magnified by the unpredictable nature of the data encoded in RDF. Additionally, current RDF stores, in general, scale poorly, which may exacerbate the performance behavior for querying and retrieving RDF triples. As a consequence, we proposed new semantic storage models for managing RDF data in relational databases to show how a state-of-the-art scaling method can be improved with ontology-based techniques for speed and high scalability.