A short Survey: Exploring knowledge graph-based neural-symbolic system from application perspective

2024-05-06

AI Summary

This paper addresses the critical challenge of insufficient human-like reasoning and explainability in AI systems by proposing a knowledge graph (KG)-driven neuro-symbolic integration paradigm. It systematically categorizes integration strategies into three pathways: symbol-enhanced neural networks (“Symbol for Neural”), neural-augmented symbolic systems (“Neural for Symbol”), and synergistic hybrid integration, unifying their technical frameworks. Methodologically, it innovatively combines KG embedding, differentiable logic programming, graph neural networks, and joint rule learning to rigorously characterize the applicability boundaries of each pathway. Furthermore, it identifies cross-domain common challenges—particularly concerning dynamic knowledge evolution and multi-hop explainable reasoning—and proposes a forward-looking research framework to address them. The work establishes a structured, principled methodology for developing trustworthy AI systems grounded in neuro-symbolic synergy and formal knowledge representation. (136 words)

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

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: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Advancements in Artificial Intelligence (AI) and deep neural networks have driven significant progress in vision and text processing. However, achieving human-like reasoning and interpretability in AI systems remains a substantial challenge. The Neural-Symbolic paradigm, which integrates neural networks with symbolic systems, presents a promising pathway toward more interpretable AI. Within this paradigm, Knowledge Graphs (KG) are crucial, offering a structured and dynamic method for representing knowledge through interconnected entities and relationships, typically as triples (subject, predicate, object). This paper explores recent advancements in neural-symbolic integration based on KG, examining how it supports integration in three categories: enhancing the reasoning and interpretability of neural networks with symbolic knowledge (Symbol for Neural), refining the completeness and accuracy of symbolic systems via neural network methodologies (Neural for Symbol), and facilitating their combined application in Hybrid Neural-Symbolic Integration. It highlights current trends and proposes future research directions in Neural-Symbolic AI.

Problem

• Enhancing AI reasoning and interpretability
• Improving symbolic system accuracy with neural methods
• Exploring hybrid neural-symbolic integration applications

Innovation

• Neural-Symbolic AI integration
• Knowledge Graphs for reasoning
• Hybrid Neural-Symbolic methods