The primary objective of this session would be to give an overview of QA over knowledge graphs. This will include common source KGs, the datasets used to evaluate QA systems, an overview of the prominent types of approaches, and familiarizing the audience with the sub-tasks (like entity linking, predicate linking) and the terminology used in the community.
We will give a practical introduction for developing and training a simple question answering system using neural networks, with a focus on learning-to-rank approach for QA in PyTorch. First, we will show how to write a basic neural model for ranking questions, how to prepare training data and how to train and evaluate the model. Then we will discuss possible improvements to the model, such as handling out-of-vocabulary words and better predicate representation.