Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement

TL;DR

This paper introduces CP-KGC, a framework that uses constrained prompts and context strategies with large language models to improve text-based knowledge graph completion, especially in semantic enhancement and data augmentation.

Contribution

The paper proposes a novel CP-KGC framework that adapts prompts to datasets and employs context constraints, improving KGC performance with LLMs while reducing computational demands.

Findings

CP-KGC enhances KGC performance across datasets.

Quantized LLMs like Qwen-7B-Chat-int4 still improve results.

Framework extends current performance limits of KGC models.

Abstract

The design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as soft prompts and contrastive learning to enhance KGC models. The effectiveness of text-based methods largely hinges on the quality and richness of the training data. Large language models (LLMs) can utilize straightforward prompts to alter text data, thereby enabling data augmentation for KGC. Nevertheless, LLMs typically demand substantial computational resources. To address these issues, we introduce a framework termed constrained prompts for KGC (CP-KGC). This CP-KGC framework designs prompts that adapt to different datasets to enhance semantic richness. Additionally, CP-KGC employs a context constraint strategy to effectively identify polysemous entities within KGC datasets. Through extensive experimentation, we have verified the effectiveness of this framework. Even after quantization, the LLM (Qwen-7B-Chat-int4) still enhances the performance of text-based KGC methods \footnote{Code and datasets are available at \href{https://github.com/sjlmg/CP-KGC}{https://github.com/sjlmg/CP-KGC}}. This study extends the performance limits of existing models and promotes further integration of KGC with LLMs.