@inproceedings{84364d1b99ac44bc8f39e4fecef5e95a,
title = "Hypergraph of Text: A Mathematical Structure for Organizing and Analyzing Big Text Data",
abstract = "Since the collective knowledge of our world is primarily encoded in massive amounts of text data, people rely on text data to get access to all kinds of useful knowledge. However, how to organize, navigate, and analyze large amounts of text data remains a difficult open challenge. To address this challenge, we propose the Hypergraph of Text (HoT), a mathematical structure for organizing and analyzing big text data. We discuss how to create HoT from large text collections and various applications of HoT. Experimentally, we show the promise of HoT by creating a HoT on a subset of Wikipedia pages covering topics in philosophy. Experiment results show the structure created by Hot has many uses such as facilitating information access via enabling flexible corpus navigation and discovering interesting topical structures.",
keywords = "Big Text Data, Hypergraph, Text Organization, Topic Analysis",
author = "Alvarez, {Dean E.} and Zhai, {Cheng Xiang}",
note = "This work is supported in part by NSF under Award # 2229612 (National AI Institute for Inclusive Intelligent Technologies for Education) and by the SRI Program and IIDAI at the University of Illinois at Urbana-Champaign.; 2024 IEEE International Conference on Big Data, BigData 2024 ; Conference date: 15-12-2024 Through 18-12-2024",
year = "2024",
doi = "10.1109/BigData62323.2024.10824995",
language = "English (US)",
series = "Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8605--8607",
editor = "Wei Ding and Chang-Tien Lu and Fusheng Wang and Liping Di and Kesheng Wu and Jun Huan and Raghu Nambiar and Jundong Li and Filip Ilievski and Ricardo Baeza-Yates and Xiaohua Hu",
booktitle = "Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024",
address = "United States",
}