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Domain-Specific Knowledge Graph Construction

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Domain-Specific Knowledge Graph Construction/ by Mayank Kejriwal.
Author:	Kejriwal, Mayank.
Description:	XIV, 107 p. 19 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-12375-8
ISBN:	9783030123758

Domain-Specific Knowledge Graph Construction
Kejriwal, Mayank.

Domain-Specific Knowledge Graph Construction[electronic resource] /by Mayank Kejriwal. - 1st ed. 2019. - XIV, 107 p. 19 illus.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

1. What is a knowledge graph? -- 2. Information Extraction -- 3. Entity Resolution -- 4. Advanced Topic: Knowledge Graph Completion -- 5. Ecosystems .

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.

ISBN: 9783030123758

Standard No.: 10.1007/978-3-030-12375-8doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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