University of Illinois Urbana-Champaign

Improving accessibility and multi-hop reasoning in knowledge graphs

Hill, Blaine

Content Files
HILL-THESIS-2024.pdf

Permalink

https://hdl.handle.net/2142/124280

Description

Title
Improving accessibility and multi-hop reasoning in knowledge graphs
Author(s)
Hill, Blaine
Issue Date
2024-04-18
Director of Research (if dissertation) or Advisor (if thesis)
Tong, Hanghang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Knowledge Graph
  • Knowledge Graph Reasoning
  • Visualization Systems
  • Transfer Learning
Abstract
Knowledge graphs have emerged as a powerful way to represent and reason over structured data and relationships. However, significant challenges persist in making the field of knowledge graph reasoning (KGR) more accessible, interpretable, and collaborative. This thesis aims to tackle these challenges through two complementary research thrusts. The first thrust introduces Ginkgo-P, a web-based platform designed to make KGR more accessible and open. Ginkgo-P provides an intuitive interface for users to visualize and interact with a wide range of core KGR algorithms spanning node recommendation, link prediction, question answering, and reinforcement learning-based reasoning. Crucially, Ginkgo-P is architected as an open platform, enabling researchers to seamlessly integrate and visualize their custom knowledge graph algorithms alongside prepackaged demonstrations. By abstracting away complexities, Ginkgo-P removes unnecessary obstacles, fostering increased collaboration and knowledge sharing within the KGR research community. The second thrust focuses on advancing the state-of-the-art in multi-hop knowledge graph reasoning using reinforcement learning techniques. A novel transfer learning approach called "Split Multi-Hop Knowledge Graph Reasoning with Reward Shaping" is proposed. This approach introduces a reward shaping mechanism that leverages pre-trained knowledge graph embeddings to estimate rewards for partial solutions, alleviating issues arising from the inherent incompleteness of knowledge graphs. Furthermore, the reward shaping framework is extended by proposing a transfer learning paradigm and investigating the integration of BERT contextualization and prompt learning techniques to improve performance by incorporating contextual information. Through the development of Ginkgo-P and the proposed Split Multi-Hop Knowledge Graph Reasoning with Reward Shaping approach, this thesis aims to contribute novel technical innovations while fostering a more open, collaborative, and accessible research environment within the KGR field.
Graduation Semester
2024-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/124280
Copyright and License Information
Copyright 2024 Blaine Hill

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois