This tutorial will give an overview of the SMORE and Scallop systems for complex reasoning over relational databases with several hands-on sessions.
Overview
Combining reasoning with deep learning techniques has received increasing attention in the community nowadays. Among recent works, graph-structured relational databases serve as the fundamental component in many reasoning tasks. However, designing effective neural methods for reasoning tasks could be challenging, as typically it would involve two problems — learning and reasoning over representations. One motivating example is to understand image content through scene graph representations.
The challenges involved in the two stages are 1) learning the representation for objects to obtain scene graphs in a weakly supervised manner; and 2) handling the noisy links when executing symbolic queries on scene graphs. These two stages are complementary while also coupled to each other for a reasoning task. In this tutorial, we will cover the reasoning over relational databases in these two stages through 1) learning representations with symbolic reasoning and 2) learning to reason over symbolic queries. For each of these two, we will present the corresponding preliminaries and recent advances in research, and provide hands-on experience on the recently open-sourced toolkits Scallop and Smore, respectively. The main goal of this tutorial is to introduce the background and recent works in the graph reasoning topic, provide demos of recent toolkits, and cover the challenges and possible future directions in the research.
Tutorial Materials
Scallop Tutorial [Colab]
SMORE Tutorial [Colab]
Organizers
