Abstract

Given a domain-specific set of concept labels, taxonomy induction is the problem of inducing a taxonomy over the concept labels. Despite its importance in problems such as e-commerce, and some algorithmic research as a consequence, practical tools for taxonomy induction and interactive visualization do not currently exist. To be truly useful, such a tool must permit a reasonable solution in a relatively unsupervised setting, and be applicable to general subsets of concept labels. In this paper, we present an unsupervised, end-to-end taxonomy induction system for arbitrary concept-labels from the e-commerce domain. Our system only takes a simple text file as input and yields a tree-like taxonomy that can be rendered on a browser, and that a non-technical user can interact with. Important components of the system can also be customized by a technically experienced user.

Date

November 8, 2021

Authors

Mayank Kejriwal, Ke Shen

Book

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages

301-304