Mining Latent Entity Structures - Chi Wang

. .

Résumé :
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.

Biographie:
Chi Wang is co-founder and president of the U.S.-China Policy Foundation and adjunct professor at Georgetown University. He was the head of the Chinese section of the Library of Congress from 1990-2004. Wang's many publications include A Compelling Journey from Peking to Washington (2011), George W. Bush and China (2009), and U.S. Congress and America-China Relations since 1989 (2001). He has been publisher of the Washington Journal of Modern China since 1992.