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        <title>Knowledge Graphs meet Moral Values</title>
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        <namePart type="given">Ioana</namePart>
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    <name type="personal">
        <namePart type="given">Graeme</namePart>
        <namePart type="family">Hirst</namePart>
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            <title>Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics</title>
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            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Manaal</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Knowledge Graphs meet Moral Values
%A Hulpu\textcommabelows, Ioana
%A Kobbe, Jonathan
%A Stuckenschmidt, Heiner
%A Hirst, Graeme
%Y Gurevych, Iryna
%Y Apidianaki, Marianna
%Y Faruqui, Manaal
%S Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F hulpus-etal-2020-knowledge
%X 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.
%U https://aclanthology.org/2020.starsem-1.8
%P 71-80

Markdown (Informal)

[Knowledge Graphs meet Moral Values](https://aclanthology.org/2020.starsem-1.8) (Hulpuș et al., *SEM 2020)

• Knowledge Graphs meet Moral Values (Hulpuș et al., *SEM 2020)

ACL

• Ioana Hulpuș, Jonathan Kobbe, Heiner Stuckenschmidt, and Graeme Hirst. 2020. Knowledge Graphs meet Moral Values. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 71–80, Barcelona, Spain (Online). Association for Computational Linguistics.