Towards Unified Neurosymbolic Reasoning on Knowledge Graphs

2025-07-04

AI Summary

Knowledge graph reasoning faces two key challenges: the fragmentation between neural and symbolic approaches, and poor adaptability across diverse reasoning scenarios. To address these, this paper proposes Tunsr, a unified neural-symbolic reasoning framework that— for the first time—integrates transductive, inductive, interpolative, and extrapolative reasoning within a single model. Its core innovations include: (i) a unified reasoning graph structure with forward logical message passing to synergistically combine neural computation and symbolic rules; (ii) the FARI algorithm, enabling dynamic rule merging and automatic induction of first-order logic rules; and (iii) attention-enhanced structural awareness. Extensive experiments across 19 benchmark datasets demonstrate that Tunsr significantly improves cross-scenario generalization, validating both the effectiveness and scalability of deep neural-symbolic integration.

Technology Category

• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Knowledge Graph (KG) reasoning has received significant attention in the fields of artificial intelligence and knowledge engineering, owing to its ability to autonomously deduce new knowledge and consequently enhance the availability and precision of downstream applications. However, current methods predominantly concentrate on a single form of neural or symbolic reasoning, failing to effectively integrate the inherent strengths of both approaches. Furthermore, the current prevalent methods primarily focus on addressing a single reasoning scenario, presenting limitations in meeting the diverse demands of real-world reasoning tasks. Unifying the neural and symbolic methods, as well as diverse reasoning scenarios in one model is challenging as there is a natural representation gap between symbolic rules and neural networks, and diverse scenarios exhibit distinct knowledge structures and specific reasoning objectives. To address these issues, we propose a unified neurosymbolic reasoning framework, namely Tunsr, for KG reasoning. Tunsr first introduces a consistent structure of reasoning graph that starts from the query entity and constantly expands subsequent nodes by iteratively searching posterior neighbors. Based on it, a forward logic message-passing mechanism is proposed to update both the propositional representations and attentions, as well as first-order logic (FOL) representations and attentions of each node. In this way, Tunsr conducts the transformation of merging multiple rules by merging possible relations at each step. Finally, the FARI algorithm is proposed to induce FOL rules by constantly performing attention calculations over the reasoning graph. Extensive experimental results on 19 datasets of four reasoning scenarios (transductive, inductive, interpolation, and extrapolation) demonstrate the effectiveness of Tunsr.

Problem

• Integrate neural and symbolic reasoning for KG
• Address diverse real-world reasoning scenarios
• Bridge representation gap between rules and networks

Innovation

• Unified neurosymbolic reasoning framework Tunsr
• Forward logic message-passing mechanism
• FARI algorithm for FOL rule induction