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This content will become publicly available on August 24, 2025

Title: Automated Mining of Structured Knowledge from Text in the Era of Large Language Models
Massive amount of unstructured text data are generated daily, ranging from news articles to scientific papers. How to mine structured knowledge from the text data remains a crucial research question. Recently, large language models (LLMs) have shed light on the text mining field with their superior text understanding and instructionfollowing ability. There are typically two ways of utilizing LLMs: fine-tune the LLMs with human-annotated training data, which is labor intensive and hard to scale; prompt the LLMs in a zero-shot or few-shot way, which cannot take advantage of the useful information in the massive text data. Therefore, it remains a challenge on automated mining of structured knowledge from massive text data in the era of large language models. In this tutorial, we cover the recent advancements in mining structured knowledge using language models with very weak supervision. We will introduce the following topics in this tutorial: (1) introduction to large language models, which serves as the foundation for recent text mining tasks, (2) ontology construction, which automatically enriches an ontology from a massive corpus, (3) weakly-supervised text classification in flat and hierarchical label space, (4) weakly-supervised information extraction, which extracts entity and relation structures.  more » « less
Award ID(s):
1956151 2118329
PAR ID:
10540606
Author(s) / Creator(s):
; ; ; ; ; ;
Editor(s):
Baeza-Yates, Ricardo; Bonchi, Francesco
Publisher / Repository:
ACM
Date Published:
Edition / Version:
1
ISBN:
9798400704901
Page Range / eLocation ID:
6644 to 6654
Subject(s) / Keyword(s):
Data Mining Structured Knowledge Text Mining Large Language Models
Format(s):
Medium: X
Location:
Barcelona Spain
Sponsoring Org:
National Science Foundation
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