AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction
Pollawat Hongwimol, Cheng Lin Yu, Haoning Shang, Zhichao Wan, Chutong Wang, Lin Gui, Y. Li, Wenhao Sun, Yi Gao
arXiv (Cornell University)
Problems Identified (4)
E-commerce Attribute Extraction Bottlenecks: Product attribute extraction in e-commerce is limited by inconsistent, incomplete, and costly-to-maintain ontologies.
Dynamic PKG Evaluation: Dynamic product-attribute knowledge graphs require evaluation of type/key validity, consolidation quality, and edge-level value assertion accuracy.
E-commerce Attribute Extraction Bottlenecks: Product attribute extraction in e-commerce is limited by inconsistent, incomplete, and costly-to-maintain ontologies.
Dynamic PKG Evaluation: Dynamic product-attribute knowledge graphs require evaluation of type/key validity, consolidation quality, and edge-level value assertion accuracy.
Proposed Solutions (5)
AutoPKG Multi-Agent LLM Framework: AutoPKG is a multi-agent LLM framework that automatically constructs product-attribute knowledge graphs from multimodal product content.
On-Demand Type and Attribute Induction: The framework induces product types and type-specific attribute keys on demand.
Multimodal Attribute Value Extraction: The framework extracts attribute values from both text and images.
Centralized Canonical Graph Consolidation: A centralized decision agent consolidates updates to maintain a globally consistent canonical graph.
Dynamic PKG Evaluation Protocol: The authors propose an evaluation protocol for dynamic product-attribute knowledge graphs.
Results (3)
High Product Type WKE:
Attribute Key WKE:
Multimodal Value Extraction F1:
Research Domain
E-commerce product-attribute knowledge graph construction