Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/132348
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Type: | Conference paper |
Title: | AutoKnow: Self-driving knowledge collection for products of thousands of types |
Author: | Dong, X.L. He, X. Kan, A. Li, X. Liang, Y. Ma, J. Xu, Y.E. Zhang, C. Zhao, T. Blanco Saldana, G. Deshpande, S. Michetti Manduca, A. Ren, J. Singh, S.P. Xiao, F. Chang, H.S. Karamanolakis, G. Mao, Y. Wang, Y. Faloutsos, C. et al. |
Citation: | Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '20), 2020, pp.2724-2734 |
Publisher: | Association for Computing Machinery |
Publisher Place: | online |
Issue Date: | 2020 |
ISBN: | 9781450379984 |
Conference Name: | ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (23 Aug 2020 - 27 Aug 2020 : virtual online) |
Statement of Responsibility: | Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han |
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. |
Keywords: | knowledge graphs; taxonomy enrichment; attribute importance; data imputation; data cleaning; synonym finding |
Description: | Applied Data Science Track Paper |
Rights: | © 2020 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. |
DOI: | 10.1145/3394486.3403323 |
Published version: | https://dl.acm.org/doi/proceedings/10.1145/3394486 |
Appears in Collections: | Computer Science publications |
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hdl_132348.pdf | Published version | 2.34 MB | Adobe PDF | View/Open |
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