# Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs

arXiv: https://arxiv.org/abs/2305.09858 · HTML: https://mlsanity.com/paper/2305.09858/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems](https://mlsanity.com/paper/2205.08084/) (2022)
- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [One-Shot Relational Learning for Knowledge Graphs](https://mlsanity.com/paper/1808.09040/) (2018)
- [Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/1911.11298/) (2019)
- [Product Knowledge Graph Embedding for E-commerce](https://mlsanity.com/paper/1911.12481/) (2019)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [Practical Text Classification With Large Pre-Trained Language Models](https://mlsanity.com/paper/1812.01207/) (2018)
- [GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations](https://mlsanity.com/paper/2208.04222/) (2022)
- [Basket Recommendation with Multi-Intent Translation Graph Neural Network](https://mlsanity.com/paper/2010.11419/) (2020)
- [Type-augmented Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/2009.07938/) (2020)
- [Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network](https://mlsanity.com/paper/2111.14036/) (2021)
- [NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation](https://mlsanity.com/paper/2202.05456/) (2022)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)

## Built on by (most cited first, 24 of 24)

- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Towards Next-Generation Recommender Systems: A Benchmark for Personalized Recommendation Assistant with LLMs](https://mlsanity.com/paper/2503.09382/) (2025)
- [Automated Construction of Theme-specific Knowledge Graphs](https://mlsanity.com/paper/2404.19146/) (2024)
- [LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following](https://mlsanity.com/paper/2308.04913/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [A Survey on Large Language Models in Multimodal Recommender Systems](https://mlsanity.com/paper/2505.09777/) (2025)
- [Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models](https://mlsanity.com/paper/2310.05628/) (2023)
- [CARTS: Collaborative Agents for Recommendation Textual Summarization](https://mlsanity.com/paper/2506.17765/) (2025)
- [Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs](https://mlsanity.com/paper/2510.23941/) (2025)
- [LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing](https://mlsanity.com/paper/2312.16351/) (2023)
- [Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectives](https://mlsanity.com/paper/2406.06475/) (2024)
- [Exploring Large Language Models for Product Attribute Value Identification](https://mlsanity.com/paper/2409.12695/) (2024)
- [Membership Inference Attacks on Recommender System: A Survey](https://mlsanity.com/paper/2509.11080/) (2025)
- [Structure-Augmented Reasoning Generation](https://mlsanity.com/paper/2506.08364/) (2025)
- [Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs](https://mlsanity.com/paper/2402.05941/) (2024)
- [Enhancing Knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metrics](https://mlsanity.com/paper/2502.05239/) (2025)
- [The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identification](https://mlsanity.com/paper/2512.12059/) (2025)
- [Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce](https://mlsanity.com/paper/2410.21237/) (2024)
- [End-to-End Personalization: Unifying Recommender Systems with Large Language Models](https://mlsanity.com/paper/2508.01514/) (2025)
- [MedicalRec: Medical recommender system for image classification without retraining](https://mlsanity.com/paper/2606.07553/) (2026)
- [Positional task conditioning for scalable defect detection across product families in large product catalogs](https://mlsanity.com/paper/2609.09567/) (2026)

## Abstract (arXiv)

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.