Jiaming Shen, Jiawei Han. Automated Taxonomy Discovery and Exploration

Cham: Springer, 2022. — 112 p.This book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human-annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today’s information era, people are inundated with vast amounts of text data. Despite their usefulness, people haven’t yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usage. Taxonomy hierarchically organizes entities and concepts. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers to topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role in science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.