
Mining Latent Entity Structures
Morgan and Claypool Life Sciences (Publisher)
Published on 30. March 2015
Book
Paperback/Softback
159 pages
978-1-62705-660-1 (ISBN)
Description
The ""big data"" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3) entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions.
More details
Series
Language
English
Place of publication
San Rafael, CA
United States
Publishing group
Morgan & Claypool Publishers
Dimensions
Height: 235 mm
Width: 187 mm
Weight
333 gr
ISBN-13
978-1-62705-660-1 (9781627056601)
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Schweitzer Classification
Content
- Acknowledgments
- Introduction
- Hierarchical Topic and Community Discovery
- Topical Phrase Mining
- Entity Topical Role Analysis
- Mining Entity Relations
- Scalable and Robust Topic Discovery
- Application and Research Frontier
- Bibliography
- Authors' Biographies
- Introduction
- Hierarchical Topic and Community Discovery
- Topical Phrase Mining
- Entity Topical Role Analysis
- Mining Entity Relations
- Scalable and Robust Topic Discovery
- Application and Research Frontier
- Bibliography
- Authors' Biographies