Title: Knowledge Reasoning with Graph Neural Networks
Ph.D. Student in Computer Science
School of Computational Science and Engineering
College of Computing
Georgia Institute of Technology
Date: Friday, April 23, 2021
Time: 10:00 am – 12:00 pm ET
Location (virtual): https://bluejeans.com/742208733
Dr. Le Song (Advisor, Mohamed bin Zayed University of Artificial Intelligence)
Dr. Chao Zhang (School of Computer Science and Engineering, Georgia Institute of Technology)
Dr. Diyi Yang (School of Interactive Computing, Georgia Institute of Technology)
Dr. Yao Xie (School of Industrial and Systems Engineering, Georgia Institute of Technology)
Dr. Arun Ramamurthy (Siemens Corporate Technology)
Knowledge reasoning is the process of drawing conclusions from existing facts and rules, which requires a range of capabilities including but not limited to understanding concepts, applying logic, and calibrating or validating architecture based on existing knowledge. With the explosive growth of communication techniques and mobile devices, much of collective human knowledge resides on the Internet today, in unstructured and semi-structured forms such as text, tables, images, videos, etc. It is overwhelmingly difficult for human to navigate the gigantic Internet knowledge without the help of intelligent systems such as search engines and question answering systems. To serve various information needs, in this thesis, we develop methods to perform knowledge reasoning over both structured and unstructured data.
This thesis attempts to answer the following research questions on the topic of knowledge reasoning:
(1) How to perform multi-hop reasoning over knowledge graphs? How should we leverage graph neural networks to learn graph-aware representations efficiently? And, how to systematically handle the noise in human questions?
(2) How to combine deep learning and symbolic reasoning in a consistent probabilistic framework? How to make the inference efficient and scalable for large-scale knowledge graphs? Can we strike a balance between the representation power and the simplicity of the model?
(3) How to build an open-domain question answering system that can reason over multiple retrieved documents? How to efficiently rank and filter the retrieved documents to reduce the noise for the downstream answer prediction module? How to propagate and assemble the information among multiple retrieved documents?
(4) How to answer the questions that require numerical reasoning over textual passages? How to enable pre-trained language models to perform numerical reasoning?
In this thesis, we explored the research questions above and discovered that graph neural networks can be leveraged as a powerful tool for various knowledge reasoning tasks over both structured and unstructured knowledge sources. On structured graph-based knowledge source, we build graph neural networks on top of the graph structure to capture the topology information for downstream reasoning tasks. On unstructured text-based knowledge source, we first identify graph-structured information such as entity co-occurrence and entity-number binding, and then employ graph neural networks to reason over the constructed graphs, working together with pre-trained language models to handle unstructured part of the knowledge source.