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Extracting Cultural Commonsense Knowledge at Scale

Authors:

Tuan-Phong Nguyen,

Simon Razniewski,

Gerhard WeikumAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 1907 - 1917

https://doi.org/10.1145/3543507.3583535

Published: 30 April 2023 Publication History

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Abstract

Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects. However, they lack knowledge about human traits and behaviors conditioned on socio-cultural contexts, which is crucial for situative AI. This paper presents Candle, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. Candle extracts CCSK assertions from a huge web corpus and organizes them into coherent clusters, for 3 domains of subjects (geography, religion, occupation) and several cultural facets (food, drinks, clothing, traditions, rituals, behaviors). Candle includes judicious techniques for classification-based filtering and scoring of interestingness. Experimental evaluations show the superiority of the Candle CCSK collection over prior works, and an extrinsic use case demonstrates the benefits of CCSK for the GPT-3 language model. Code and data can be accessed at https://candle.mpi-inf.mpg.de/.

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Cited By

Seth AAhuja SBali kSitaram S(2024)DOSA: A Dataset of Social Artifacts from Different Indian Geographical SubculturesSSRN Electronic Journal10.2139/ssrn.4756716Online publication date: 2024
https://doi.org/10.2139/ssrn.4756716
Nguyen TRazniewski SWeikum GSerra ESpezzano F(2024)Cultural Commonsense Knowledge for Intercultural DialoguesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679768(1774-1784)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679768
Sheng YZeng WTang J(2024)Negation: An Effective Method to Generate Hard NegativesWeb and Big Data. APWeb-WAIM 2023 International Workshops10.1007/978-981-97-2991-3_3(25-35)Online publication date: 9-May-2024
https://doi.org/10.1007/978-981-97-2991-3_3
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Index Terms

Extracting Cultural Commonsense Knowledge at Scale
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

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Published: 30 April 2023

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Cited By

Seth AAhuja SBali kSitaram S(2024)DOSA: A Dataset of Social Artifacts from Different Indian Geographical SubculturesSSRN Electronic Journal10.2139/ssrn.4756716Online publication date: 2024
https://doi.org/10.2139/ssrn.4756716
Nguyen TRazniewski SWeikum GSerra ESpezzano F(2024)Cultural Commonsense Knowledge for Intercultural DialoguesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679768(1774-1784)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679768
Sheng YZeng WTang J(2024)Negation: An Effective Method to Generate Hard NegativesWeb and Big Data. APWeb-WAIM 2023 International Workshops10.1007/978-981-97-2991-3_3(25-35)Online publication date: 9-May-2024
https://doi.org/10.1007/978-981-97-2991-3_3
Li JLi JSu Y(2024)A Map of Exploring Human Interaction Patterns with LLM: Insights into Collaboration and CreativityArtificial Intelligence in HCI10.1007/978-3-031-60615-1_5(60-85)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60615-1_5
Hidalgo RSalah NChandra Jetty RJetty AVarde A(2024)Personalizing Text-to-Image Diffusion Models by Fine-Tuning Classification for AI ApplicationsIntelligent Systems and Applications10.1007/978-3-031-47721-8_44(642-658)Online publication date: 10-Jan-2024
https://doi.org/10.1007/978-3-031-47721-8_44

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