提出可任意层叠神经与符号计算的图结构新框架，突破传统固定流程限制。
DeepGraphLog for Layered Neurosymbolic AI
- 将符号表示建模为图，用图神经网络处理，实现神经与符号任意层叠。
- 在规划、知识图谱补全等任务中有效捕捉复杂关系依赖，性能优于现有方法。
- 适合研究图神经网络与符号推理融合的学者，尤其关注可解释性与灵活性。
神经符号人工智能（NeSy）旨在结合神经网络的统计能力与符号推理的可解释性。然而，当前框架如DeepProbLog采用固定流程，符号推理总在神经处理之后，难以建模复杂依赖，尤其在图结构等不规则数据上表现受限。本文提出DeepGraphLog，扩展ProbLog引入图神经谓词，支持多层神经符号推理，允许神经与符号组件以任意顺序层叠。不同于无法通过神经方法进行符号推理的DeepProbLog，DeepGraphLog将符号表示视为图，由图神经网络（GNN）处理。我们在规划、远距离监督下的知识图谱补全及GNN表达能力任务上验证了该框架。结果表明，DeepGraphLog能有效捕获复杂关系依赖，克服现有系统关键局限。该框架拓展了神经符号人工智能在图结构领域的适用性，提供了更灵活且表达力更强的神经符号集成方案。
原文摘要 · Abstract (English)
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy frameworks like DeepProbLog enforce a fixed flow where symbolic reasoning always follows neural processing. This restricts their ability to model complex dependencies, especially in irregular data structures such as graphs. In this work, we introduce DeepGraphLog, a novel NeSy framework that extends ProbLog with Graph Neural Predicates. DeepGraphLog enables multi-layer neural-symbolic reasoning, allowing neural and symbolic components to be layered in arbitrary order. In contrast to DeepProbLog, which cannot handle symbolic reasoning via neural methods, DeepGraphLog treats symbolic representations as graphs, enabling them to be processed by Graph Neural Networks (GNNs). We showcase the capabilities of DeepGraphLog on tasks in planning, knowledge graph completion with distant supervision, and GNN expressivity. Our results demonstrate that DeepGraphLog effectively captures complex relational dependencies, overcoming key limitations of existing NeSy systems. By broadening the applicability of neurosymbolic AI to graph-structured domains, DeepGraphLog offers a more expressive and flexible framework for neural-symbolic integration.
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