Knowledge Graph Embeddings: From Theory to Practice
September 04, 1:45 pm - 5:00 pm (CEST)
Speakers: Luca Costabello, Sumit Pai, Nicholas McCarthy, Adrianna Janik
Tutorial website: https://kge-tutorial-ecai2020.github.io/
Agenda: Knowledge Graph Embeddings: From Theory to Practice
Knowledge graph embeddings are supervised learning models that learn vector representations of nodes and edges of labeled, directed multigraphs. We describe their design rationale, and explain why they are receiving growing attention within the burgeoning graph representation learning community. We highlight their limitations, open research directions, and real-world applicative scenarios. Besides a theoretical overview, we also provide a hands-on session, where we show how to use such models in practice.
Luca Costabello is research scientist in Accenture Labs Dublin. His research interests span knowledge graphs applications, machine learning for graphs, and explainable AI.
Sumit Pai is a research engineer at Accenture Labs Dublin. His research interests include knowledge graphs, representational learning, computer vision and its applications. Sumit has also worked as an engineer (Computer Vision) at Robert Bosch, India. He has done his Masters in Neural Information Processing from University of Tübingen, Germany.
Nicholas McCarthy is a research scientist at Accenture Labs. He holds a Bachelors in Computer Science and a PhD in Medical Imaging from University College Dublin. Prior to joining Accenture Labs Nicholas worked at the INSIGHT Research Center and the Complex and Adaptive Systems Laboratory in UCD, where he was a Teaching Assistant for a number of BSc and MSc Courses including: Intro. to A.I., Intro. to Image Analysis, Compiler Construction, and Software Engineering. He is a contributor to the open source AmpliGraph library for knowledge graph embeddings, and has significant experience applying these methods in industrial applications. His research interests include computer vision, and graph representation learning. Recent work has been published at SIGGRAPH and IAAI.
Adrianna Janik is a research engineer at Accenture Labs Dublin. Her research interests are interpretability in machine learning, deep learning, and recently knowledge graphs. She has double Masters in Data Science with a minor in entrepreneurship from the European Institute of Innovation and Technology (EIT), at the University of Nice – Sophia Antipolis and at the Royal Institute of Technology, Stockholm. During studies, she did her thesis internship at the Montreal Institute for Learning Algorithms. She also has a Bachelors in Control Engineering and Robotics from the Wroclaw University of Technology and used to work as a software engineer at Nokia.