The vast amount of data available on the web has led to the need for effective retrieval techniques to transform that data into usable machine knowledge. But the creation of integrated knowledge, especially knowledge about the same entity from different web data sources, is a challenging task requiring the solving of interoperability problems. This book addresses the problem of knowledge retrieval and integration from heterogeneous web sources, and proposes a holistic semantic knowledge retrieval and integration approach to creating knowledge graphs on-demand from diverse web sources. Semantic Web Technologies have evolved as a novel approach to tackle the problem of knowledge integration from heterogeneous data, but because of the Extraction-Transformation-Load approach that dominates the process, knowledge retrieval and integration from web data sources is either expensive, or full physical integration of the data is impeded by restricted access. Focusing on the representation of data from web sources as pieces of knowledge belonging to the same entity which can then be synthesized as a knowledge graph helps to solve interoperability conflicts and allow for a more cost-effective integration approach, providing a method that enables the creation of valuable insights from heterogeneous web data. Empirical evaluations to assess the effectiveness of this holistic approach provide evidence that the methodology and techniques proposed in this book help to effectively integrate the disparate knowledge spread over heterogeneous web data sources, and the book also demonstrates how three domain applications of law enforcement, job market analysis, and manufacturing, have been developed and managed using the approach.