Domain-Specific Knowledge Graph Construction

  • Mayank Kejriwal

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Mayank Kejriwal
    Pages 1-7
  3. Mayank Kejriwal
    Pages 9-31
  4. Mayank Kejriwal
    Pages 33-57
  5. Mayank Kejriwal
    Pages 59-74
  6. Mayank Kejriwal
    Pages 75-87
  7. Back Matter
    Pages 89-107

About this book


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.


Knowledge Graphs Information Extraction Domain Discovery Web Corpora Machine Learning Natural Language Processing Data Mining Knowledge Discory Semantic Web Wrapper Induction Querying Entity-Centric Search Entity Resolution Knowledge Graph Construction Knowledge Graph Completion Knowledge Graph Embeddings Probabilistic Soft Logic

Authors and affiliations

  • Mayank Kejriwal
    • 1
  1. 1.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA

Bibliographic information

  • DOI
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-030-12374-1
  • Online ISBN 978-3-030-12375-8
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site