PhD Dissertation Defense - Mehrnoosh Mirtaheri

Title: Toward Learning and Forecasting with Temporal Knowledge Graphs
 
Date and Time: Tuesday, January 22nd, 2025 - 1:00p - 3:00p
 
Location: SAL 213
 
Committee: Aram Galstyan (Chair), Emilio Ferrara (Tenured Faculty), Fred Morstatter, Antonio Ortega (External Faculty
 
Abstract:  Temporal knowledge graphs (TKGs) model real-world relationships between entities over time, enabling insight extraction from unstructured data. While powerful for various applications, TKGs are inherently limited by incompleteness and noise, making their completion and forecasting crucial research areas.
This thesis tackles the key challenges in TKG forecasting: relation sparsity in large-scale graphs, continuous integration of new data while preserving existing knowledge, and entity evolution as new entities emerge and existing ones appear in novel contexts. Through novel methodological frameworks, this research demonstrates improved predictive accuracy, robustness to data sparsity, and adaptability to evolving data, validated through extensive evaluation on both standard benchmark and real-world datasets.
 
Zoom Link: https://usc.zoom.us/j/96220815599

Location: Henry Salvatori Computer Science Center (SAL) - 213

Audiences: Everyone Is Invited

Contact: Mehrnoosh Mirtaheri