AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Gabriel Blanco Saldana: Amazon; Saurabh Deshpande: Amazon; Xin Luna Dong: Amazon; Xiang He: Amazon; Andrey Kan: Amazon; Xian Li: Amazon; Yan Liang: Amazon; Jun Ma: Amazon; Alexandre Michetti Manduca: Amazon; Jay Ren: Amazon; Surender Pal Singh: Amazon; Fan Xiao: Amazon; Yifan Ethan Xu: Amazon; Chenwei Zhang: Amazon; Tong Zhao: Amazon; Haw-Shiuan Chang: University of Massachusetts Amherst; Giannis Karamanolakis: Columbia University; Yuning Mao: University of Illinois at Urbana Champaign; Yaqing Wang: State University of New York at Buffalo; Christos Faloutsos: Carnegie Mellon University; Andrew McCallum: University of Massachusetts Amherst; Jiawei Han: University of Illinois at Urbana Champaign
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products.
We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.
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