Soft-Unification in Deep Probabilistic Logic

Published: 21 Sept 2023, Last Modified: 14 Jan 2024NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: neuro-symbolic AI, probabilistic logic, embeddings
TL;DR: We propose a probabilistic integration of embeddings in logic programming using soft-unification.
Abstract: A fundamental challenge in neuro-symbolic AI is to devise primitives that fuse the logical and neural concepts. The Neural Theorem Prover has proposed the notion of soft-unification to turn the symbolic comparison between terms (i.e. unification) into a comparison in embedding space. It has been shown that soft-unification is a powerful mechanism that can be used to learn logic rules in an end-to-end differentiable manner. We study soft-unification from a conceptual point and outline several desirable properties of this operation. These include non-redundancy in the proof, well-defined proof scores, and non-sparse gradients. Unfortunately, these properties are not satisfied by previous systems such as the Neural Theorem Prover. Therefore, we introduce a more principled framework called DeepSoftLog based on probabilistic rather than fuzzy semantics. Our experiments demonstrate that DeepSoftLog can outperform the state-of-the-art on neuro-symbolic benchmarks, highlighting the benefits of these properties.
Supplementary Material: pdf
Submission Number: 6363