Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2501.18119v1)

Abstract: Due to the presence of the natural gap between Knowledge Graph (KG) structures and the natural language, the effective integration of holistic structural information of KGs with LLMs has emerged as a significant question. To this end, we propose a two-stage framework to learn and apply quantized codes for each entity, aiming for the seamless integration of KGs with LLMs. Firstly, a self-supervised quantized representation (SSQR) method is proposed to compress both KG structural and semantic knowledge into discrete codes (\ie, tokens) that align the format of language sentences. We further design KG instruction-following data by viewing these learned codes as features to directly input to LLMs, thereby achieving seamless integration. The experiment results demonstrate that SSQR outperforms existing unsupervised quantized methods, producing more distinguishable codes. Further, the fine-tuned LLaMA2 and LLaMA3.1 also have superior performance on KG link prediction and triple classification tasks, utilizing only 16 tokens per entity instead of thousands in conventional prompting methods.

• The paper demonstrates that SSQR compresses KG data into discrete tokens that align with LLM inputs, enhancing tasks like link prediction.
• It employs a two-stage framework combining Graph Convolutional Networks with vector quantization to capture both KG structure and semantics.
• Experimental results on WN18RR show a 9.28% MRR improvement, underscoring SSQR's scalability and efficiency in real-world applications.

Integration of Knowledge Graphs with LLMs through Self-supervised Quantized Representations

In the domain of artificial intelligence, the convergence of Knowledge Graphs (KGs) and LLMs represents an emerging frontier, particularly due to the intrinsic gap between KG structures and natural language processing frameworks. The paper "Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with LLMs" presents a novel approach that focuses on bridging this gap using a two-stage framework centered around a self-supervised quantized representation (SSQR) method. This framework aims to compress the structural and semantic elements of KGs into discrete codes, enabling their integration with LLMs.

The Framework and Methodology

The proposed framework operates in two fundamental stages:

1. Quantized Representation Learning: The self-supervised quantized representation method compresses KG data into discrete token-like codes that are alignable with natural language formats. This is achieved by employing a Graph Convolutional Network (GCN) to model KG structures while using vector quantization techniques to encapsulate discrete entity codes. The core innovation here is the use of self-supervised learning to distill both the structural and semantic components of KGs, thus creating quantized codes that effectively summarize the full breadth of KG information.
2. Seamless Integration with LLMs: Once learned, the quantized codes are used to construct KG instruction-following data, which can be directly input into LLMs. This means that the learned codes can act as features, allowing for operations such as link prediction and triple classification without extensive modifications to the LLM framework. The SSQR thus leverages these codes to bridge the existing gap between KGs and LLMs, allowing for smooth coherent integration.

Experimental Validation and Comparative Performance

The experimental results featured in the paper demonstrate the superior performance of the SSQR method against existing unsupervised quantized methods such as NodePiece and EARL. For instance, on the WN18RR dataset, the SSQR achieved substantial improvements in mean reciprocal rank (MRR) and Hits@10 metrics, demonstrating the robust distinguishability and representation capacity of the learned codes. Specifically, the SSQR improved MRR by approximately 9.28% on WN18RR, compared to the best baseline methods.

Furthermore, the fine-tuned models, LLaMA2 and LLaMA3.1, exhibited superior performance in tasks such as KG link prediction and triple classification, utilizing merely 16 tokens per entity. This efficient use of tokens highlights the computational benefits and scalability of the SSQR framework over traditional text-based or continuous embedding methods that often require thousands of tokens and face challenges in handling extensive KG data.

Implications and Future Prospects

The implications of this work are significant both in practical and theoretical dimensions. Practically, the ability to integrate KGs with LLMs using quantized representations opens up new avenues for enhancing the factual accuracy and reasoning capabilities of AI systems. This approach addresses challenges associated with knowledge hallucination and provides a method for incorporating structured data into the natural language understanding of LLMs. Theoretically, the work lays down a framework for exploring the symbiosis between symbolic knowledge representations and neural model capabilities.

Future developments could explore unified frameworks for handling diverse KG tasks, further enhancing LLMs’ ability to leverage structured knowledge effectively. This could involve scaling the SSQR approach for larger and more complex KGs and investigating its application across different domains beyond the current scope.

In conclusion, the proposed SSQR method marks a step towards the seamless integration of structured knowledge frameworks with LLMs, mitigating the representation gap that has traditionally hindered such integrations. The demonstrated improvements in performance metrics underscore the potential of this method to become a cornerstone in the development of more informed and reliable AI systems.