Overview
- 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 work 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.
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (5 chapters)
Keywords
- 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
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 anaccessible 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.
Authors and Affiliations
Bibliographic Information
Book Title: Domain-Specific Knowledge Graph Construction
Authors: Mayank Kejriwal
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-030-12375-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-12374-1Published: 15 March 2019
eBook ISBN: 978-3-030-12375-8Published: 04 March 2019
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIV, 107
Number of Illustrations: 19 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Information Storage and Retrieval, Information Systems Applications (incl. Internet), Probability and Statistics in Computer Science