A talk by Isabelle Augenstein, Nathan Benaich, Amy Hodler, Katariina Kari, Fabio Petroni and Giuseppe Futia
What does graph have to do with machine learning?
A lot, actually. And it goes both ways.
Machine learning can help bootstrap and populate knowledge graphs.
The information contained in graphs can boost the efficiency of machine learning approaches.
Machine learning, and its deep learning subdomain, make a great match for graphs. Machine learning on graphs is still a nascent technology, but one which is full of promise.
Amazon, Alibaba, Apple, Facebook and Twitter are just some of the organizations using this in production, and advancing the state of the art.
Domain knowledge can effectively help a deep learning system bootstrap its knowledge, by encoding primitives instead of forcing the model to learn these from scratch.
Machine learning can effectively help the semantic modeling process needed to construct knowledge graphs, and consequently populate them with information.
