Mining Latent Entity Structures

Abstract

e "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. is 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. is book also introduces applications enabled by the mined structures and points out some promising research directions.

Natural language indexing for pedoinformatics

“…The self-reflective world of scientometrics is beginning to see scientific text mining as a scientific activity in itself (Mayr and Scharnhorst, 2015). Since text mining is primarily taught in computer science departments, computer science literature often comprises a favorite target of analyses, and applications to internet text are especially scrutinized by computer scientists, and others, to discern social connections and shared intents as well as for other kinds of web analytics (Gupta and Lehal, 2009;Yu et al, 2010;Miner et al, 2012;Sun and Han, 2012;Kiritchenko et al, 2014;Wang and Han, 2015).…”

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

“…Although a relatively new field of statistics, topic modeling is rapidly gaining attention across a range of application domains including, for example, a variety of machine learning and data mining tasks (Cook andKrishnan 2015, Wang andHan 2015); analysis of political texts and processes (Grimmer 2010, Roberts et al 2014, Koltsova and Shcherbak 2015, Lucas et al 2015; the examination of social networks through writings in social media (Tang and Li 2015); assessing scholarly impact (Gerrish and Blei 2010) and the content of scholarly publications (Griffiths and Steyvers 2004); reviewing consumer research publications ; and evaluating online health service delivery (Chen et al 2015).…”