Location: Virtual
The Joint CARTE (University of Toronto) and University of Seoul Applied AI/DS seminar series welcomes Professor Mark Fox.
Registration: Please register here.
Abstract: “Ontologies are a useful tool for representing knowledge in a structured and formal way, and this can be particularly useful in the context of machine learning. An ontology typically defines a set of concepts and the relationships between them, providing a common vocabulary that can be used to describe a domain of knowledge. This can help to improve the accuracy and consistency of data used in machine learning, as well as providing a way to integrate data from multiple sources. Additionally, the structured nature of ontologies can make it easier for machine learning algorithms to extract and understand the relevant information from the data.” ChatGPT (7 December 2022)
This seminar will provide an introduction to Ontologies, their application to the structuring of city information, and how they open up core decision process in cities to machine learning.
Bio: Dr. Fox is a Distinguished Professor of Urban Systems Engineering, Professor of Industrial Engineering and Computer Science, Director of the Urban Data Centre in the School of Cities, and Founding Director of the Centre for Social Services Engineering at the University of Toronto. Dr. Fox pioneered the field of Constraint-Directed Scheduling and the development of Ontologies for modelling Enterprises. He is currently investigating the application of Ontologies to the representation and utilization of urban knowledge, including social services, transportation and water & sanitation. He is actively involved in the development of international standards city data based on his urban ontology research, including ISO/IEC 21972 “Upper level ontology for smart city indicators” and the ISO/IEC 5087 series of “City data model” standards. Dr. Fox is also an entrepreneur having co-founded Carnegie Group Inc. in 1984, a software company that specialized in Artificial Intelligence-based systems.