Welcome to Knowledge graph construction

Description: The ability to represent and reason with knowledge in a structured, machine-readable format is rapidly becoming essential for modern artificial intelligence applications. Knowledge graphs (KGs) offer a powerful paradigm for achieving this, enabling applications ranging from intelligent search and question answering to drug discovery and personalized medicine. However, constructing high-quality KGs is a complex undertaking, requiring expertise in information extraction, entity resolution, and data integration – challenges that lie at the intersection of knowledge engineering, information retrieval, natural language processing, and increasingly, domain-specific knowledge.
This course addresses the problem of building robust knowledge graphs from diverse data sources. The course will start with a comprehensive introduction to the principles and techniques underlying KG construction, starting from the basic KG concepts, (e.g. data models and query languages), and continuing with advanced topics such as information extraction from free text, entity matching, and KG ingestion. The course will also discuss approaches based on large language models (LLMs) for automating the extraction of structured data from text. As a use-case to illustrate the application of the notions and techniques, the course will consider real-world applications within the medical domain.

For additional information, updates, and registration, please refer to AAU PhD Moodle via the link provided on the right side of this page.

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