Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping

Wonduk Seo, Taesub Shin, Hyunjin An, Dokyun Kim, Seunghyun Lee

Abstract: Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in e-commerce, especially when explicit identifiers are missing and product names vary widely across platforms. Rule-based heuristics and keyword similarity often misclassify products by overlooking subtle distinctions in brand, specification, or bundle configuration. To overcome these limitations, we propose Question-to-Knowledge ($Q 2 K$), a multi-agent framework that leverages Large Language Models (LLMs) for reliable SKU mapping. Q2K integrates: (1) a Reasoning Agent that generates targeted disambiguation questions, (2) a Knowledge Agent that resolves them via focused web searches, and (3) a Deduplication Agent that reuses validated reasoning traces to reduce redundancy and ensure consistency. A human-in-the-loop mechanism further refines uncertain cases. Experiments on real-world consumer goods datasets show that Q2K surpasses strong baselines, achieving higher accuracy and robustness in difficult scenarios such as bundle identification and brand-origin disambiguation. By reusing retrieved reasoning instead of issuing repeated searches, Q2K balances accuracy with efficiency, offering a scalable and interpretable solution for product integration11Code is available at: https://github.com/viralpick/paper-q2k-artifact

External IDs:dblp:conf/bigdataconf/SeoSAKL25