Xian Li is a Senior Applied Scientist at Amazon leading the science effort of building retail knowledge graphs. Before joining Amazon, she was a machine learning scientist at LinkedIn, where she was a major contributor to build LinkedIn’s knowledge base of business entities and filed 6 patents in three years. Her research interests include truth finding in structured and unstructured data sources, data quality, and knowledge management.
2021 Talk: AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
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 a large number of categories, as well as large and constantly growing number of products. We present 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.