Representation Learning in Knowledge Graphs

Mastercard is looking to make transactions simple and secure for its customers and vendors across the globe by harnessing the power of graph learning. In this project, the power of graph-based deep learning will be utilized to develop a system that can detect fraud entities, anomalous communities, etc. This track will enable card users and online buyers and sellers to enjoy safe and secure digital transactions. The final goal of knowledge representation learning (KRL) from graphs is to obtain a universal information rich embedding that can be used under multiple applications. The benefit to the partner organization includes a greater understanding of the domain of Knowledge Graphs, identification of gaps in the current area of research, and the possession of a system that can detect fraud entities with better accuracy compared to the state-of-the-art systems in the field.

Faculty Supervisor:

Esam Abdel-Raheem

Student:

Partner:

Mastercard

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Windsor

Program: