The World Wide Web Consortium (W3C) RDB2RDF Working Group (RDB2RDF-WG) defined two standards, Direct Mapping (DM) and Relational Database to Resource Description Framework (RDF) Mapping Language (R2RML) for mapping Relational Databases (RDBs) to RDF. Direct Mapping directly maps the RDB schema to RDF using a collection of simple transformations, whereas R2RML is a language for manually created mappings from RDB tables to RDF output. The manual creation of mapping is complex, error-prone, and time-consuming, where any single mistake could produce an invalid output document. In this thesis we introduce three contributions: In the first contribution, a new Graphical Ultimate Processor (GUP) is proposed for mapping from RDBs to RDF. The proposed mapping processor is called RDB2RDF-GUP, but for simplicity, we shall represent RDB2RDF-GUP by RUP. This processor acts as a standalone tool with a Graphical User Interface (GUI) that facilitates the mapping process and supports a diversity of other features. This new processor is a useful tool for integrating the databases in Semantic Web applications that incorporate all data formats into a combined knowledge model. Through a small set of GUI screens, RUP enables the users to perform the most required tasks by selecting from the available lists most of the time rather than writing. This processor is simple and very useful for domain experts and semi-experts. Our results showed that the proposed processor, RUP, outperforms other existing processors in usability and the number of supported features. In the second contribution, the proposed system is used to extract the structured data from a database based on a target ontology. It is applied to music databases because of their popularity on the web. Since the Semantic Web is built upon reusing existing ontologies, this system is going to use the existing musical ontology defined in the Music Ontology Specification. This specification provides the concepts and properties used to describe music besides other ontologies that could use in conjunction with it, such as (foaf, dc, timeline, event, etc.). The resulting music data, in RDF format, can be published and linked with existing musical data on the web. This method contributes to the music web of data and allows Semantic Web clients to access detailed structured information about musical data easily. In the third contribution, we apply the aforementioned proposed work to build a Semantic Web application that creates a new semantic social community called Socialpedia. It links the already existing social public information to the newly public ones. This information is linked with different pieces of information on the web to construct a new immense data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce machine-understandable content. This content shows the promise of using integrated data to improve Web search and Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data from traditional users known as contributors or participants, linking data from existing social networks, extracting structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic search engine.