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Record W4401540932 · doi:10.31219/osf.io/z8uqr

Ontologies for Sustainability: Theoretical Challenges

2024· preprint· en· W4401540932 on OpenAlexfundno aff
Giorgio A. Ubbiali, Andrea Borghini, Matthew Lange

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
FundersOffice of Advanced CyberinfrastructureUniversity at BuffaloEuropean CommissionDipartimenti di EccellenzaMcGill UniversityUniversità degli Studi di MilanoU.S. Department of AgricultureNational Science Foundation
KeywordsSustainabilityIDEF5OntologySustainability sciencePolysemyKnowledge managementSustainability organizationsComputer scienceEngineering ethicsBusinessManagement scienceEngineeringEcologyProcess ontologyEpistemologyArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

The lack of sustainable practices in key sectors of human societies is a global concern, held by some to threaten the life of the whole planet. To date, we have found that no studies review ontologies in terms of sustainability. This paper aims to fill such a gap. In Section 1, we outline the three major challenges associated with sustainability: 1) the polysemy of the term; 2) the relationship between sustainability and sustainable development; and 3) the complexity underlying sustainability. In Section 2, we review the main accomplishments achieved so far by ontologies to meet these theoretical challenges. We devote special attention to ontologies belonging to the OBO Foundry due to the pivotal role these play in the field of applied ontology. We provide a List of Sustainability Ontologies documenting the resources we assessed. To conclude, we advance a potential trajectory to further develop an OBO Foundry-compliant family of sustainability ontologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0050.027
Scholarly communication0.0170.047
Open science0.0040.010
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.318
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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Same topicSemantic Web and OntologiesFrench-language works237,207