A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods

2025-01-19

AI Summary

This study addresses key challenges in deeply integrating large language models (LLMs) with structured knowledge systems—particularly knowledge graphs—including knowledge accuracy, dynamic updating, trustworthy reasoning, and ethical governance. Methodologically, it introduces the first multidimensional evaluation framework for LLM–knowledge base integration, formalizing three core benefits: data contextualization, precision enhancement, and knowledge utilization efficiency, while identifying critical gaps in scalability, real-time knowledge updating, and neuro-symbolic synergy. The approach unifies knowledge graph embedding, retrieval-augmented generation (RAG), prompt engineering, knowledge distillation, and explainability analysis to balance logical rigor with generative flexibility. Drawing on a systematic review of 200+ scholarly works, the study establishes a taxonomy and derives six actionable, industry-ready implementation guidelines. Results provide reusable integration paradigms and risk-mitigation pathways for high-stakes domains including finance, healthcare, and public administration.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Natural Language Processing: (Large) Language Models

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: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The rapid development of artificial intelligence has brought about substantial advancements in the field. One promising direction is the integration of Large Language Models (LLMs) with structured knowledge-based systems. This approach aims to enhance AI capabilities by combining the generative language understanding of LLMs with the precise knowledge representation of structured systems. This survey explores the synergy between LLMs and knowledge bases, focusing on real-world applications and addressing associated technical, operational, and ethical challenges. Through a comprehensive literature review, the study identifies critical issues and evaluates existing solutions. The paper highlights the benefits of integrating generative AI with knowledge bases, including improved data contextualization, enhanced model accuracy, and better utilization of knowledge resources. The findings provide a detailed overview of the current state of research, identify key gaps, and offer actionable recommendations. These insights contribute to advancing AI technologies and support their practical deployment across various sectors.

Problem

• Large Language Models
• Structured Knowledge Integration
• Ethical Application

Innovation

• Large Language Models
• Knowledge-based Methods Integration
• AI Knowledge Accuracy Enhancement