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Addressing Label Sparsity With Class-Level Common Sense for Google Maps

Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations,...

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Autores principales: Welty, Chris, Aroyo, Lora, Korn, Flip, McCarthy, Sara M., Zhao, Shubin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967349/
https://www.ncbi.nlm.nih.gov/pubmed/35372829
http://dx.doi.org/10.3389/frai.2022.830299
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author Welty, Chris
Aroyo, Lora
Korn, Flip
McCarthy, Sara M.
Zhao, Shubin
author_facet Welty, Chris
Aroyo, Lora
Korn, Flip
McCarthy, Sara M.
Zhao, Shubin
author_sort Welty, Chris
collection PubMed
description Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth.
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spelling pubmed-89673492022-03-31 Addressing Label Sparsity With Class-Level Common Sense for Google Maps Welty, Chris Aroyo, Lora Korn, Flip McCarthy, Sara M. Zhao, Shubin Front Artif Intell Artificial Intelligence Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth. Frontiers Media S.A. 2022-03-16 /pmc/articles/PMC8967349/ /pubmed/35372829 http://dx.doi.org/10.3389/frai.2022.830299 Text en Copyright © 2022 Welty, Aroyo, Korn, McCarthy and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Welty, Chris
Aroyo, Lora
Korn, Flip
McCarthy, Sara M.
Zhao, Shubin
Addressing Label Sparsity With Class-Level Common Sense for Google Maps
title Addressing Label Sparsity With Class-Level Common Sense for Google Maps
title_full Addressing Label Sparsity With Class-Level Common Sense for Google Maps
title_fullStr Addressing Label Sparsity With Class-Level Common Sense for Google Maps
title_full_unstemmed Addressing Label Sparsity With Class-Level Common Sense for Google Maps
title_short Addressing Label Sparsity With Class-Level Common Sense for Google Maps
title_sort addressing label sparsity with class-level common sense for google maps
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967349/
https://www.ncbi.nlm.nih.gov/pubmed/35372829
http://dx.doi.org/10.3389/frai.2022.830299
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