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

arXiv: https://arxiv.org/abs/2006.13473 · HTML: https://mlsanity.com/paper/2006.13473/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 30 of 31)

- [Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2110.13522/) (2021)
- [Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks](https://mlsanity.com/paper/2205.10282/) (2022)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [PAM: Understanding Product Images in Cross Product Category Attribute Extraction](https://mlsanity.com/paper/2106.04630/) (2021)
- [Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models](https://mlsanity.com/paper/2302.06229/) (2023)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph](https://mlsanity.com/paper/2209.15214/) (2022)
- [K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce](https://mlsanity.com/paper/2104.06960/) (2021)
- [Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction](https://mlsanity.com/paper/2206.14264/) (2022)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://mlsanity.com/paper/2305.07633/) (2023)
- [What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding](https://mlsanity.com/paper/2305.14814/) (2023)
- [Enabling LLM Knowledge Analysis via Extensive Materialization](https://mlsanity.com/paper/2411.04920/) (2024)
- [e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce](https://mlsanity.com/paper/2207.00208/) (2022)
- [Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact](https://mlsanity.com/paper/2308.14217/) (2023)
- ["Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops](https://mlsanity.com/paper/2107.03256/) (2021)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction](https://mlsanity.com/paper/2406.06839/) (2024)
- [Locally Aggregated Feature Attribution on Natural Language Model Understanding](https://mlsanity.com/paper/2204.10893/) (2022)
- [REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2206.05123/) (2022)
- [Expanding Knowledge Graphs with Humans in the Loop](https://mlsanity.com/paper/2212.05189/) (2022)
- [HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks](https://mlsanity.com/paper/2501.12857/) (2025)
- [Intent-based Product Collections for E-commerce using Pretrained Language Models](https://mlsanity.com/paper/2110.08241/) (2021)
- [Unsupervised Keyphrase Extraction via Interpretable Neural Networks](https://mlsanity.com/paper/2203.07640/) (2022)
- [Learning to Personalize Recommendation based on Customers' Shopping Intents](https://mlsanity.com/paper/2305.05279/) (2023)
- [Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model](https://mlsanity.com/paper/2512.07232/) (2025)
- [AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction](https://mlsanity.com/paper/2604.16950/) (2026)
- [OASYS: Domain-Agnostic Automated System for Constructing Knowledge Base from Unstructured Text](https://mlsanity.com/paper/2207.07597/) (2022)
- [BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration](https://mlsanity.com/paper/2606.04909/) (2026)
- [JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications](https://mlsanity.com/paper/2606.28070/) (2026)

## Abstract (arXiv)

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.