Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs

ArXi:2505.20948v3 Announce Type: replace Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such as clinical diagnosis and scientific discovery. However, due to a lack of controllability, a single observation may yield numerous plausible but redundant or irrelevant hypotheses on large-scale knowledge graphs. To address this limitation, we