The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as
knowledge graphs
. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner.
The book describes a
timely topic
for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a
useful reference,
as well as an
accessible but rigorous overview
of this body of work. The book presents
interdisciplinary connections
when possible to engage researchers looking for new ideas or synergies. The book also appeals to
practitioners in industry and data scientists
since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.
