Temporal link prediction in the wild
File(s)
Author(s)
Ong, Chuan Ming (Ryan)
Type
Thesis
Abstract
In a data-driven world, knowledge graphs become essential in organising and navigating the web of explicit and implicit relationships within data. Knowledge graphs store factual information on real-world objects and concepts and the relationship between entities, offering us a holistic view and understanding of the domain of interest. Despite their potential to revolutionise information management and enhance various industrial applications, traditional knowledge graphs often lack the temporal information necessary to capture the dynamic nature of real-world data. Additionally, many existing methodologies for encoding knowledge graphs are developed to handle traditional static knowledge graphs. As such, they are ineffective in handling an ever-evolving temporal knowledge graph with new emerging entities and relations over time. This reduces the applicability of knowledge graphs in real-world scenarios.
This limitation highlights the critical need for methodologies to effectively encode temporal knowledge graphs, enabling reliable integration of evolving knowledge graphs into industrial applications. Addressing this important challenge serves as the overarching goal of this PhD thesis.
This thesis aims to develop a novel method that can accurately encode unseen entities and relations in ever-evolving temporal knowledge graphs such that knowledge graphs can reliably infuse up-to-date information into industrial applications.
This limitation highlights the critical need for methodologies to effectively encode temporal knowledge graphs, enabling reliable integration of evolving knowledge graphs into industrial applications. Addressing this important challenge serves as the overarching goal of this PhD thesis.
This thesis aims to develop a novel method that can accurately encode unseen entities and relations in ever-evolving temporal knowledge graphs such that knowledge graphs can reliably infuse up-to-date information into industrial applications.
Date Issued
2024-09-02
Date Awarded
01/03/2025
License URL
Advisor
Serban, Ovidiu
Publisher Department
Department of Computing
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)