AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce
This work addresses the problem of manual and complex knowledge graph construction for e-commerce platforms, offering a scalable solution for information retrieval and recommendation systems, though it is incremental as it builds on existing LLM methods.
The paper tackles the challenge of constructing product knowledge graphs from unstructured e-commerce data by introducing an AI agent-driven framework that automates the process, achieving over 97% property coverage and minimal redundancy on a real-world dataset of air conditioner descriptions.
The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KGs) offer a structured, interpretable format to organize such data, yet constructing product-specific KGs remains a complex and manual process. This paper introduces a fully automated, AI agent-driven framework for constructing product knowledge graphs directly from unstructured product descriptions. Leveraging Large Language Models (LLMs), our method operates in three stages using dedicated agents: ontology creation and expansion, ontology refinement, and knowledge graph population. This agent-based approach ensures semantic coherence, scalability, and high-quality output without relying on predefined schemas or handcrafted extraction rules. We evaluate the system on a real-world dataset of air conditioner product descriptions, demonstrating strong performance in both ontology generation and KG population. The framework achieves over 97\% property coverage and minimal redundancy, validating its effectiveness and practical applicability. Our work highlights the potential of LLMs to automate structured knowledge extraction in retail, providing a scalable path toward intelligent product data integration and utilization.