Link Prediction for Hypothesis Generation: An Active Curriculum Learning Infused Temporal Graph-Based Approach

Abstract

We frame scientific hypothesis generation as a link prediction task over a temporal knowledge graph. By combining active learning and curriculum learning strategies, the model learns to prioritise promising hypothesis candidates as new evidence arrives — making the system increasingly efficient as it encounters more of the literature.