Ramachandran, Deepa2025-12-152025-12-152025-12-15https://hdl.handle.net/10222/85565PTRM models temporal uncertainty in knowledge graphs through distributional event embeddings and probabilistic overlap computation, replacing fixed temporal thresholds with learned probability distributions. Experiments on ICEWS and GDELT datasets demonstrate 27-40 percentage point improvements over state-of-the-art baselines with strong calibration (ECE=0.148-0.363), advancing temporal reasoning capabilities for applications requiring uncertainty-aware predictions under variable temporal dynamicsExisting approaches for constructing knowledge graphs (KGs) from text sources effectively capture structural relationships but often struggle to represent the temporal uncertainty inherent in event-based information. Their temporal encoding typically relies on exact timestamp matching, which limits the ability to handle natural temporal variation among events. This thesis introduces Probabilistic Temporal Relationship Modeling (PTRM), a framework that represents events as temporal distributions to capture uncertainty in timing and relationship propagation. PTRM employs distributional embeddings that jointly encode semantic and temporal uncertainty, enabling soft temporal matching through probabilistic overlap computation. Experiments involving 200,000 events from the ICEWS and GDELT datasets demonstrate that PTRM substantially outperforms existing baselines, including temporal knowledge graph embedding methods and classical approaches, while maintaining strong calibration properties essential for decision-critical applications. The results demonstrate that PTRM bridges the gap between rigid timestamp-based methods and the temporal flexibility required for real-world reasoning, achieving state-of-the-art performance in temporal relationship inference under uncertainty.enTemporal knowledge graphsuncertainty quantification