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.


The goal of the tutorial is to provide answers to the following questions:


[Slides available soon]

Target Audience

The target audience is the general ECAI audience who is interested in knowledge representation and reasoning, machine learning, and natural language processing.

That includes artificial intelligence scientists, engineers, and students familiar with neural networks fundamentals and eager to know insights of graph representation learning for knowledge graphs. Researchers from graph-based knowledge representation (e.g. Semantic Web, Linked Data) and NLP also qualify as target audience.

The tutorial is of interest for either academic research and industry practitioners.

Other Details

Format: Standard (Half-day)

Presentation Style: Slide deck presentation and Jupyter notebook/Colab tutorials for the hands-on session.


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.