DISSERTATION DEFENSE

(will take place in hybrid fashion; both physical and virtual)

Author : Manas Gaur

Advisor : Dr. Amit P. Sheth

Date : March 25, 2022

Time 10:30 am

Location : Seminar Room, AI Institute, 5th Floor,

    1112 Greene Street (Science and Technology Building)
     Columbia, South Carolina-29208

Virtual Zoom Link

              Meeting ID: 844 013 9296
         Passcode: 12345

Abstract:

In DARPA’s view on three waves of AI, the first wave of AI termed symbolic AI, focused on explicit knowledge. The current second wave of AI is termed statistical AI. Deep learning techniques have been able to exploit large amounts of data and massive computational power to improve human levels of performance in narrowly defined tasks. Separately, knowledge graphs emerged as a powerful tool to capture and exploit an extensive amount and variety of explicit knowledge to make algorithms better understand the content, and enable the next generation of data processing, such as in semantic search. After initial hesitancy about the scalability of the knowledge creation process, the last decade has seen significant growth in developing and applying knowledge, usually in the form of knowledge graphs (KG). Examples range from the use of DBPedia in IBM’s Watson to Google Knowledge Graph in Google Semantic Search, and the application of ProteinBank in AlphaFold, recognized by many as the most significant AI breakthrough, as well as numerous domain-specific knowledge have been applied in improving AI methods in diverse domains such as medicine and healthcare, finance, manufacturing, and defense.

Now, we herald towards the third wave of AI built on what is termed as the neuro-symbolic approach that combines the strengths of statistical and symbolic AI. Combining the respective powers and benefits of using knowledge graphs and deep learning is particularly attractive. This has led to the development of an approach we have called knowledge-infused (deep) learning. This dissertation will advance the currently limited forms of combining the knowledge graphs and deep learning, called shallow and semi-infusion, with a more advanced form called deep-infusion, that will support stronger interleaving of more variety of knowledge at different levels of abstraction with layers in a deep learning architecture.

This dissertation will investigate the knowledge-infusion strategy in two important ways. The first is to infuse knowledge to make any classification task explainable. The second is to achieve explainability in any natural language generation tasks. I will demonstrate the effective strategies of knowledge infusion that bring five characteristic properties in any statistical AI model: (1) Context Sensitivity, (2) Handling Uncertainty and Risk, (3) Interpretable in learning, (4) User-level Explainability, and (5) Transferability across natural language understanding (NLU) tasks. Along with proven methodological contributions in AI made by the dissertation, I will show their applications for open-domain and close-domain NLU.
Furthermore, the dissertation will showcase the utility of incorporating diverse forms of knowledge: linguistic, commonsense, broad-based, and domain-specific. As the dissertation illustrates the success in various domains, achieving state-of-the-art in specific applications, and significant contributions towards improving the state of machine intelligence, I will walk through careful steps to prevent errors arising due to knowledge infusion. Finally, for future directions, I will discuss two exciting areas of research where knowledge infusion would be pivotal to propel machine understanding.