Building Knowledge from Text: Methods, Models, and Practice

Large amounts of knowledge are created and stored every day as unstructured text, including articles, reports, notes, guidelines, and other sources from the World Wide Web. While rich in meaning, this information is often difficult to analyze systematically and remains unused when it comes to learning, reasoning, and decision making.

This training explores how unstructured text can be transformed into structured and usable knowledge. It examines the key challenges involved, such as ambiguity, context, scale, and credibility, and discusses approaches for extracting, organizing, and connecting information in ways that support understanding, insight, and automation.

The session introduces traditional and modern methods for working with natural language, including AI-based techniques and knowledge representations, and shows how they can help bridge the gap between text and insights. The focus is on concepts, patterns, and real-world considerations, making the course relevant for researchers, practitioners, and anyone interested in turning text into knowledge.