Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance

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 ecsonomic 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.

SMEs and the Sustainability Challenge: Digital Shadow Enabling Smart Decision Making

“…On top of these challenges, there is a profound need for transparency of the data used in assessment of performance, and this need will increase dramatically when methods applied becomes more detailed and closer to reality. The current state of the art does not accommodate this, but promising approaches are under development (Hansen et al 2020), and these are consistent with the approach recommended in this chapter.…”

“…However, the SME should not get frustrated by the method discussions, because the issues under discussion are not related to the accounting of activities but rather to the methods for how to account for the related emissions(Bo Pedersen Weidema et al 2019). This means that the company basically should work with collecting data with a robust strategy, which is collecting relevant and traceable raw data on exchanges and emission (Hansen et al 2020;Ghose et al 2021), instead of collecting calculated emission-data, e.g. EPD based carbon footprints.…”

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

“…In our future work, we plan to expand our experiments to comprehensive datasets and combining RPT and PGT into a single hybrid transformation approach. Furthermore, based on our previous work, we plan to investigate how processing advanced queries over provenance-enhanced datasets can be improved [12][13][14]24], how querying heterogeneous provenance-enhanced graph data can be embedded in scalable ecosystems [29], how evolving knowledge graphs can be supported [20], and how interoperability between RDF-star and property graphs can be achieved [28].…”

“…The test cases (8)(9)(10)(11)(12)(13)(14)(15) and their variations are 12 cases in total (see Table 1), and we present some examples from Cases 8, 9, and 10. The complete list of cases can be found in Section A.2 of Appendix A.…”

A core ontology for modeling life cycle sustainability assessment on the Semantic Web

“…Adding provenance increases the transparency and trustworthiness of a dataset. We linked the BONSAI ontology to the W3C Prov-O ontology (Belhajjame et al, 2013;Hansen et al, 2020).Each of the classes are linked to other classes using one or more predicates. Twelve new predicates were also defined in the BONSAI ontology.…”