Knowledge graphs meet moral values

Hulpus, Ioana ; Kobbe, Jonathan ; Stuckenschmidt, Heiner ; Hirst, Graeme

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URN: urn:nbn:de:bsz:180-madoc-577630
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics : Barcelona, Spain (Online), December 2020
Page range: 71-80
Conference title: SemEval Starsem 2020
Location of the conference venue: Online
Date of the conference: 12.-13.12.2020
Publisher: Gurevych, Iryna ; Apidianaki, Marianna ; Faruqui, Manaal
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core {--} such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.

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