Unified Large Language Models and Knowledge Graphs

Background

In recent years, numerous research achievements have emerged in the fields of natural language processing and artificial intelligence. Notably, large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated remarkable performance. However, despite their excellent generalization abilities, these models are often criticized for their black-box nature, which limits their capacity to effectively capture and access factual knowledge. On the other hand, knowledge graphs (KGs) like Wikipedia and Huapu store vast amounts of factual knowledge in a structured format, but the construction and evolution of knowledge graphs are highly complex processes. Consequently, researchers have proposed combining large language models with knowledge graphs to leverage the strengths of both and achieve complementarity.

Source

This paper was published in the IEEE Transactions on Knowledge and Data Engineering, Volume 36, Issue 7, in July 2024. The primary authors include Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. The article was collaboratively completed by scholars from institutions such as Griffith University, Monash University, Nanyang Technological University, Beijing University of Technology, and Hefei University of Technology.

Research Content

This review article proposes future development pathways for the unification of large language models and knowledge graphs and summarizes existing research efforts. The paper outlines three main frameworks: KG-Enhanced LLMs, LLM-Augmented KGs, and Synergized LLMs + KGs.

KG-Enhanced LLMs

Research Process: This section proposes the introduction of knowledge graphs during the pre-training and inference stages of large language models. By integrating knowledge graphs during the pre-training phase, the model can learn the factual knowledge within knowledge graphs throughout the training process. Utilizing knowledge graphs during the inference phase can significantly enhance the model’s capacity to handle domain-specific knowledge. Additionally, knowledge graphs can be used to enhance the model’s interpretability, elucidating the reasoning process and generated content of the model.

Main Results: Several experiments have proven that integrating knowledge graphs significantly improves the performance of large language models across various natural language processing tasks. Incorporating knowledge graphs during the pre-training phase enables the model to more effectively learn factual knowledge, while using knowledge graphs during inference helps the model to acquire the latest knowledge when generating text, increasing the model’s accuracy and reliability.

Conclusion: Integrating knowledge graphs not only improves the performance of large language models but also enhances their interpretability, making them widely applicable in high-risk scenarios such as medical diagnosis and legal judgments.

LLM-Augmented KGs

Research Process: This section explores how to enhance various tasks within knowledge graphs through the use of large language models, including embedding, completion, construction, graph-to-text generation, and question answering. By using large language models as text encoders, the representation performance of knowledge graphs can be improved. Large language models can also be employed in tasks such as entity discovery, coreference resolution, and relationship extraction during knowledge graph construction, thereby enhancing the completeness and quality of the knowledge graphs.

Main Results: Utilizing large language models, researchers have achieved more efficient knowledge graph embedding and completion, as well as more accurate knowledge graph construction. In many tasks, methods based on large language models have significantly outperformed traditional approaches, demonstrating the enormous potential of large language models in processing textual information and enriching knowledge graph representations.

Conclusion: Large language models hold significant importance in enriching and enhancing the performance of knowledge graphs, effectively addressing the limitations of traditional methods in knowledge graph processing.

Synergized LLMs + KGs

Research Process: This section aims to combine large language models and knowledge graphs to realize synergistic effects in knowledge representation and reasoning. By introducing additional knowledge graph fusion modules, joint models for knowledge representation are designed. Synergistic reasoning employs both text and knowledge graph inputs to enable more efficient knowledge integration and reasoning.

Main Results: Research indicates that synergistic reasoning models combining large language models and knowledge graphs perform exceptionally well across multiple tasks. Through bidirectional attention mechanisms and graph neural networks, deeper interactions between text and knowledge graphs can be achieved, significantly enhancing the model’s reasoning capabilities and interpretability.

Conclusion: Synergizing large language models and knowledge graphs can notably enhance their respective performances in knowledge representation and reasoning, addressing the limitations of using either approach alone.

Research Highlights

The highlight of this paper lies in its comprehensive roadmap, covering various integration methods of large language models and knowledge graphs and systematically summarizing existing research efforts. Furthermore, the article identifies challenges and directions for future research, such as using knowledge graphs to detect hallucination problems in large language models, editing knowledge within large language models, and dealing with multi-modal knowledge graphs.

Future Directions

The article proposes several future research directions, including: 1. Utilizing knowledge graphs to detect hallucination problems in large language models, thereby enhancing their reliability. 2. Editing knowledge within large language models to maintain dynamic knowledge updates. 3. Developing large language models capable of understanding graph structures and multi-modal knowledge graphs. 4. Exploring bidirectional reasoning between synergized large language models and knowledge graphs to achieve more powerful knowledge representation and reasoning capabilities.

Conclusion

This review article summarizes existing research achievements, proposes future research roadmaps and directions, and holds significant reference value. In the future, further integration of large language models and knowledge graphs will undoubtedly propel the development of natural language processing and artificial intelligence, leading to broader application scenarios. Hence, the research on the unification of large language models and knowledge graphs bears substantial scientific and practical value.