Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance

Emil Riis Hansen, Matteo Lissandrini, Agneta Ghose, Søren Løkke, Christian Thomsen, Katja Hose

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The final authenticated version is available online at 10.1007/978-3-030-62466-8\_24.

Abstract :

Life Cycle Sustainability Analysis (LCSA) studies the complex processes describing product life cycles and their impact on the environment, economy, and society. Effective and transparent sustainability assessment requires access to data from a variety of heterogeneous sources across countries, scientific and economic sectors, and institutions. Moreover, given their important role for governments and policymakers, the results of many different steps of this analysis should be made freely available, alongside the information about how they have been computed in order to ensure accountability. In this paper, we describe how Semantic Web technologies in general and PROV-O in particular, are used to enable transparent sharing and integration of datasets for LCSA. We describe the challenges we encountered in helping a community of domain experts with no prior expertise in Semantic Web technologies to fully overcome the limitations of their current practice in integrating and sharing open data. This resulted in the first nucleus of an open data repository of information about global production. Furthermore, we describe how we enable domain experts to track the provenance of particular pieces of information that are crucial in higher-level analysis.

Additional material is available on the BONSAI community portal and GitHub.

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and
“Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance.”
Proceedings of International Semantic Web Conference (ISWC 2020) (pp. 378-394). 

@inproceedings{HLGLTH:Transparent,
author="Hansen, Emil Riis and Lissandrini, Matteo and Ghose, Agneta
  and L{\o}kke, S{\o}ren and Thomsen, Christian and Hose, Katja",
title="Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance",
booktitle="The Semantic Web -- ISWC 2020",
year="2020",
publisher="Springer International Publishing",
pages="378--394",
isbn="978-3-030-62466-8"
doi = {10.1007/978-3-030-62466-8\_24},
}