Mining latent entity structures from massive unstructured and interconnected data

Jiawei Han, Chi Wang

Abstract: 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 but interconnected data. Mining latent structured information around entities uncovers sematic structures from massive unstructured data and hence enables many high-impact applications. In this tutorial, we summarize the closely related literature in database systems, data mining, Web, information extraction, information retrieval, and natural language processing, overview a spectrum of data-driven methods that extract and infer such latent structures, from an interdisciplinary point of view, and demonstrate how these structures support entity discovery and management, data understanding, and some new database applications. We present three categories of studies: mining conceptual, topical and relational structures. Moreover, we present case studies on real datasets, including research papers, news articles and social networks, and show how interesting and organized knowledge can be discovered by mining latent entity structures from these datasets.