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Advancements in Knowledge Graph Reasoning: Innovative Approaches to Complex Logical Query Answering
PhD Thesis Proposal Defence
Title: "Advancements in Knowledge Graph Reasoning: Innovative Approaches to
Complex Logical Query Answering"
by
Mr. Jiaxin BAI
Abstract:
Complex logical query answering on knowledge graphs (KGs) has emerged as a
crucial component of knowledge graph reasoning. This thesis presents a
comprehensive framework for advancing KG reasoning through multiple novel
approaches across diverse knowledge graph types. We introduce Query2Particles
(Q2P), a particle-based embedding method that effectively handles distributed
answer sets in incomplete KGs by encoding queries into multiple particle
embeddings. To address numerical reasoning, we develop the Number Reasoning
Network (NRN), which integrates numerical attribute processing with
traditional entity-relation reasoning. We further propose Sequential Query
Encoding (SQE), transforming computational graphs into sequences for more
efficient and accurate query processing. Our work extends to eventuality
knowledge graphs through Complex Eventuality Query Answering (CEQA),
incorporating implicit temporal and logical constraints via Memory-Enhanced
Query Encoding (MEQE). For practical applications, we develop the Logical
Session Graph Transformer (LSGT) to understand cross-session user intentions
in recommendation systems. Additionally, we advance abductive reasoning
through our Reinforcement Learning from Knowledge Graph (RLF-KG) approach,
enabling robust logical hypothesis generation. Extensive experiments on
benchmark datasets demonstrate that our methods consistently achieve
state-of-the-art performance across various tasks, from basic query answering
to complex reasoning scenarios. These contributions collectively advance the
field of knowledge graph reasoning, providing powerful tools for complex
query answering in real-world applications.
Date: Wednesday, 4 December 2024
Time: 4:00pm - 6:00pm
Venue: Room 3494
Lifts 25/26
Committee Members: Dr. Yangqiu Song (Supervisor)
Prof. Nevin Zhang (Chairperson)
Dr. Dongdong She
Dr. Xiaomin Ouyang