@inproceedings{hongwimol-etal-2026-autopkg,
    title = "{A}uto{PKG}: An Automated Framework for Dynamic {E}-commerce Product-Attribute Knowledge Graph Construction",
    author = "Hongwimol, Pollawat  and
      Shang, Haoning  and
      Wang, Chutong  and
      Wan, Zhichao  and
      Gao, Yi  and
      Li, Yuanming  and
      Gui, Lin  and
      Sun, Wenhao  and
      Yu, Cheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.766/",
    pages = "15622--15650",
    ISBN = "979-8-89176-395-1",
    abstract = "Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. We present AutoPKG, a multi-agent Large Language Model (LLM) framework that automatically constructs a Product-attribute Knowledge Graph (PKG) from multimodal product content. AutoPKG induces product types and type-specific attribute keys on demand, extracts attribute values from text and images, and consolidates updates through a centralized decision agent that maintains a globally consistent canonical graph. We also propose an evaluation protocol for dynamic PKGs that measures type/key validity and consolidation quality, as well as edge-level accuracy for value assertions after canonicalization. On a large real-world marketplace catalog dataset from Lazada (Alibaba), AutoPKG achieves up to 0.953 Weighted Knowledge Efficiency (WKE) for product types, 0.724 WKE for attribute keys, and 0.531 edge-level F1 for multimodal value extraction. Across three public benchmarks, we improve edge-level exact-match F1 by 0.152 and yield a 0.208 precision gain on the attribute extraction application. Online A/B tests show that AutoPKG-derived attributes increase Gross Merchandise Value (GMV) in Badge (+3.81{\%}), Search (+5.32{\%}), and Recommendation (+7.89{\%}), supporting AutoPKG{'}s practical value in production."
}