Findability, Semantic Modeling, and Comp

In this course, students will explore designing effective information and knowledge systems, focusing on semantic models and computational classification techniques to enhance findability and facilitate analytics. The curriculum covers a range of foundational concepts and methods, including computational thinking, agile requirements-gathering methodologies, use cases, epics, user stories, audience profiles, personas, and user experience design decisions. Students will learn to organize data using semantic modeling techniques, such as taxonomies, ontologies, and database schemas, and apply various classification methods to improve system functionality.
 

Practical assignments will provide students with hands-on experience in organizing and enriching data. A model-driven approach (MDE) will guide design decisions on data organization, storage, analysis, and interaction. By the end of the course, students will be equipped to address specific use cases using the tools, methods, and models learned throughout the semester. No programming background is required, but students will use tools like Excel, Power BI, PowerPoint, Optimal Workshop, and Protégé for organizing data, exploring classification techniques, and authoring semantic models. This course offers a valuable learning opportunity for those interested in creating efficient information retrieval systems and contributing to the future of information and knowledge access.