AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce

• URL: http://arxiv.org/abs/2511.11017v1
• Date: Fri, 14 Nov 2025 07:09:13 GMT
• Title: AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce
• Authors: Dimitar Peshevski, Riste Stojanov, Dimitar Trajanov,
• Abstract summary: This paper introduces a fully automated, AI agent-driven framework for constructing product knowledge graphs directly from unstructured product descriptions.<n>We evaluate the system on a real-world dataset of air conditioner product descriptions.
• Score: 0.05882087655172317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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