Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not only computationally intensive, but also the irrelevant portion negatively impact the accuracy of the application. Hence, finding the relevant portion of the Web of data for domain-specific applications has become a paramount issue. Identifying the relevant portion of the Web of data consists of two sub-tasks; 1) find the relevant datasets that contain knowledge on the domain of interest, and 2) extract the subgraph representing domain of interest from the knowledge graphs that represent multiple domains (e.g., DBpedia, YAGO, Freebase). In this talk, I will discuss both data-driven and knowledge-driven approaches to solve these two sub-tasks. The domain-specific subgraphs extracted by our approach were 80% less in size in terms of the number of paths compared to original KG and resulted in more than tenfold reduction of required computational time for domain-specific tasks, yet produced better accuracy on domain-specific applications. We believe that this work can significantly contribute for utilizing knowledge graphs for domain-specific applications, specially with the explosive growth in the creation of knowledge graphs.