Papers by Mohan Timilsina

1 papers
KGE Calibrator: An Efficient Probability Calibration Method of Knowledge Graph Embedding Models for Trustworthy Link Prediction (2025.emnlp-main)

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Challenge: Existing methods for probability calibration of knowledge graph embedding models are ill-suited for KGEs.
Approach: They propose a method to calibrate knowledge graph embedding models for ranking-based link prediction using a Jump Selection Strategy and Multi-Binning Scaling to enhance reliability.
Outcome: Experiments show that the KGEC outperforms existing calibration methods in terms of effectiveness and efficiency.

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