ASDG - An AI-based framework for automatic classification of impact on the SDGs

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MARC

Sanchez-Roncero, A. (author)

ASDG - An AI-based framework for automatic classification of impact on the SDGs

Article/chapterEnglish2022

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2022-11-18
New York, NY, USA :Association for Computing Machinery (ACM),2022
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LIBRIS-ID:oai:DiVA.org:kth-328972
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-328972URI
https://doi.org/10.1145/3560107.3560128DOI

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Language:English
Summary in:English

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Subject category:kon swepub-publicationtype

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QC 20230614
Achieving the Sustainable Development Goals of the United Nations is the primary goal of the 2030 Agenda. A critical step towards that objective is identifying if the scientific production is going in this way. Funders must do a manual recognition, impacting accuracy, scalability, and objectiveness. For this reason, we propose in this work an AI-based model for the automatic classification of scientific papers based on their impacts on the SDGs. The training database consists of manually extracted texts from the UN page. After preprocessing these texts, we train three models: NMF, LDA, and Top2Vec. The output of these models is the probability of a paper being associated with each SDG. We then combine their scores by implementing a voting function to take advantage of their inherently different mathematical nature. To validate this methodology, we use the database provided by Vinuesa et al., Nature Communications 11, with more than 150 papers labeled with at least 1 SDG. Using only the abstracts, we correctly identify a of the SDGs presented in a paper, while a better is obtained when fetching the complete paper information. Moreover, we find that the other identified SDGs which were not labeled are also related to the text contents. We recognize that more training files are required for the other cases since they are based on more complex human reasoning. We open-source these databases and trained models to enable future investigation in this field and allow public institutions to use this tool.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
deep learning
gaze detection
LDA
NMF
sustainable developement goals
text tagging
Top2Vec
Database systems
Functions
Paper
Sustainable development
Automatic classification
Critical steps
Sustainable developement goal
United Nations

Added entries (persons, corporate bodies, meetings, titles ...)

Garibo-I-Orts, O. (author)
Conejero, J. A. (author)
Eivazi, HamidrezaKTH,Linné Flow Center, FLOW,Teknisk mekanik(Swepub:kth)u1gfi4i7 (author)
Rosenberg, EmelieKTH,Linné Flow Center, FLOW,Teknisk mekanik(Swepub:kth)PI000000 (author)
Nerini, Francesco FusoKTH,Energisystem,Kth, Climate Action Centre(Swepub:kth)u1ncfuqi (author)
Garcia-Martinez, J. (author)
Hoyas, S. (author)
Vinuesa, RicardoKTH,Linné Flow Center, FLOW,Strömningsmekanik och Teknisk Akustik,Kth, Climate Action Centre(Swepub:kth)u12xffui (author)
KTHLinné Flow Center, FLOW (creator_code:org_t)

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In:ACM International Conference Proceeding SeriesNew York, NY, USA : Association for Computing Machinery (ACM), s. 119-123

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

Articles in the publication: ACM Internationa ...

By the university: Royal Institute of Technology

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