Ensemble Size Variations and Relation Extraction Consistency in Product Knowledge Graphs

Abstract

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 us

Research Question

What is the impact of varying the size of the AI agent ensemble on the consistency of extracted relations in product knowledge graphs, measured by F1-score on a held-out test set of unstructured product data?

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