Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems. However, relation labeling in KGs remains a challenging task due to the dynamic nature of e-commerce domains and the associated cost of human labor. Recently, breakthroughs in Large Language Models (LLMs) have shown surprising results in numerous natural language processing tasks. In this paper, we conduct an empirical study of LLMs for relation labeling in e-commerce KGs, investigating their powerful learning capabilities in natural language and effectiveness in predicting relations between product types with limited labeled data. We evaluate various LLMs, including PaLM and GPT-3.5, on benchmark datasets, demonstrating their ability to achieve competitive performance compared to humans on relation labeling tasks using just 1 to 5 labeled examples per relation. Additionally, we experiment with different prompt engineering techniques to examine their impact on model performance. Our results show that LLMs significantly outperform existing KG completion models in relation labeling for e-commerce KGs and exhibit performance strong enough to replace human labeling.

翻译：知识图谱通过提供实体及其关系的结构化信息（如产品间或产品类别间的互补与替代关系），在增强电商系统性能方面发挥关键作用，这些关系可应用于推荐系统。然而，由于电商领域的动态特性及人工标注成本，知识图谱中的关系标注始终是一项具有挑战性的任务。近年来，大语言模型的突破性进展已在诸多自然语言处理任务中展现出令人瞩目的成果。本文针对电商知识图谱的关系标注任务，对大语言模型进行了实证研究，探究其在自然语言处理中的强大学习能力，以及利用有限标注数据预测产品类别间关系的有效性。我们评估了包括PaLM和GPT-3.5在内的多种大语言模型，在基准数据集上的实验表明，通过仅使用每类关系1至5个标注样例，这些模型即可在关系标注任务中取得与人类相媲美的竞争性能。此外，我们实验了多种提示工程技术，以检验其对模型性能的影响。研究结果表明，在电商知识图谱的关系标注任务中，大语言模型显著优于现有知识图谱补全模型，其性能足以替代人工标注。