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.