AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

TL;DR

AutoKnow is an automated system that constructs comprehensive product knowledge graphs across thousands of categories by leveraging novel techniques and customer behavior data, addressing challenges like data sparsity and heterogeneity.

Contribution

The paper introduces AutoKnow, a scalable, automatic system with innovative methods for taxonomy, property extraction, and anomaly detection in product knowledge graphs.

Findings

Operated over 11,000 product types

Effectively handles data sparsity and heterogeneity

Integrates customer behavior logs for enhanced knowledge extraction

Abstract

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.