Improving Soft Unification with Knowledge Graph Embedding Methods

Xuanming Cui, Chionh Wei Peng, Adriel Kuek, Ser-Nam Lim

Keywords: Neuro-Symbolic, Knowledge Graph Embedding

Abstract: Neural Theorem Provers (NTPs) present a promising framework for neuro-symbolic reasoning, combining end-to-end differentiability with the interpretability of symbolic logic programming. However, optimizing NTPs remains a significant challenge due to their complex objective landscape and gradient sparcity. On the other hand, Knowledge Graph Embedding (KGE) methods offer smooth optimization with well-defined learning objectives but often lack interpretability. In this work, we propose several strategies to integrate the strengths of NTPs and KGEs. By incorporating KGE objectives into the NTP framework, we demonstrate substantial improvements in both accuracy and computational efficiency.

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

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Submission Number: 3869