Build a Question Answering System Overnight

@ ESWC 2019

With this tutorial, we aim to provide the participants with an overview of the field of Question Answering over Knowledge Graphs, insights into commonly faced problems, its recent trends and developments. In doing so, we hope to provide a suitable entry point for the people new to this field, and ease their process of making informed decisions while creating their own QA systems. At the end of the tutorial, the audience would have hands-on experience of developing a working deep learning based QA system.
Session Details
Introduction to Question Answering

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.

Hands-On: Deep Learning based Simple QA
Requirements: [OPTIONAL] linux distro; python; numpy; pytorch

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.