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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. 

Today a tremendous amount of geospatial knowledge is hidden in massive volumes of text data. To facilitate flexible and powerful geospatial analysis and applications, we introduce a new architecture: geospatial knowledge hypercube, a multi-scale, multidimensional knowledge structure that integrates information from geospatial dimensions, thematic themes and diverse application semantics, extracted and computed from spatial-related text data. To construct such a knowledge hypercube, weakly supervised language models are leveraged for automatic, dynamic and incremental extraction of heterogeneous geospatial data, thematic themes, latent connections and relationships, and application semantics, through combining a variety of information from unstructured text, structured tables, and maps. The hypercube lays a foundation for many knowledge discovery and in-depth spatial analysis, and other advanced applications. We have deployed a prototype web application of proposed geospatial knowledge hypercube for public access at: https://hcwebapp.cigi.illinois.edu/.