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Mining Latent Entity Structures [electronic resource] / by Chi Wang, Jiawei Han.

By: Wang, Chi [author.].
Contributor(s): Han, Jiawei [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Data Mining and Knowledge Discovery: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015.Description: XI, 147 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031019074.Subject(s): Data mining | Statistics  | Data Mining and Knowledge Discovery | StatisticsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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