Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909897385312256 |
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| author | Seo, Wonduk Shin, Taesub An, Hyunjin Kim, Dokyun Lee, Seunghyun |
| author_facet | Seo, Wonduk Shin, Taesub An, Hyunjin Kim, Dokyun Lee, Seunghyun |
| contents | Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in ecommerce, 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 (Q2K), 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 integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping Seo, Wonduk Shin, Taesub An, Hyunjin Kim, Dokyun Lee, Seunghyun Artificial Intelligence Computation and Language Human-Computer Interaction Information Retrieval Multiagent Systems Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in ecommerce, 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 (Q2K), 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 integration. |
| title | Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Information Retrieval Multiagent Systems |
| url | https://arxiv.org/abs/2509.01182 |