Information searching techniques are rapidly developing as the World Wide Web (WWW) evolves. Along with the development of information technologies, the need for acquiring domain knowledge bases, accessing data sources and discovering insights increases. The advancements in knowledge discovery, information management and artificial intelligence require faster data processing, storing more data and developing more intelligent applications. This study provides an information discovery and data integration approach for linked open data in the semantic web. Using semantics embedded in ontologies, data available in knowledge bases can be enhanced to better serve the information needs of users. The entity relationships between resources and resource hierarchies represented as linked open data in semantic web provide semantically rich insights about the data and facilitates knowledge discovery. Graph theory methods can be utilized to enrich the features of data sets in semantic web. In this study, we propose an approach for integrating isolated data sources with semantic web by using ontologies to make them available for information discovery and enhancing the features of semantic data by using graph theory techniques.