RePlanIT Ontology for Digital Product Passports of ICT: Laptops and Data Servers

Tracking #: 3826-5040

Authors: 
Anelia Kurteva
Carlo van der Valk
Kathleen McMahon
Alessandro Bozzon
Ruud Balkenende

Responsible editor: 
Aldo Gangemi

Submission type: 
Full Paper
Abstract: 
The increasing digitisation that we have witnessed in the past few years has resulted in increased Information and Communications Technology (ICT) hardware manufacturing, which is not sustainable due to the growing demand for critical materials and the greenhouse emissions associated with it. A solution is transitioning to a circular economy (CE). To facilitate this paradigm shift, and boost the data economy and digital innovation in the field, the European Union has introduced the concept of digital product passports (DPPs), which should provide information about a product's lifetime to bring more transparency into supply chains. However, several challenges, namely the lack of findable, accessible, interoperable, reusable (FAIR) ICT and materials data and tools to support its interpretation for decision-making by both humans and machines, are at hand. Utilising Semantic Web technologies such as ontologies and knowledge graphs is a possible solution. Although the ontology work in the ICT and materials domains has been on the rise, there is a lack of a unified semantic model that can capture the complex, heterogeneous cross-domain data needed for building DPPs of ICT devices such as laptops and data servers. Motivated by this, we present the RePlanIT ontology for ICT DPPs, which captures knowledge on several levels - ICT device, hardware components, materials and the CE itself. RePlanIT's specification is based on a literature survey, interviews and inputs from domain experts from both industry and academia. The ontology, its utilisation for building a knowledge graph of DPPs of laptops and data servers and its application have been successfully validated in a real-world case focusing on supporting more sustainable ICT procurement in government.
Full PDF Version: 
Tags: 
Reviewed

Decision/Status: 
Accept

Solicited Reviews:
Click to Expand/Collapse
Review #1
Anonymous submitted on 15/May/2025
Suggestion:
Accept
Review Comment:

This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include (1) originality, (2) significance of the results, and (3) quality of writing. Please also assess the data file provided by the authors under “Long-term stable URL for resources”. In particular, assess (A) whether the data file is well organized and in particular contains a README file which makes it easy for you to assess the data, (B) whether the provided resources appear to be complete for replication of experiments, and if not, why, (C) whether the chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate for long-term repository discoverability, and (4) whether the provided data artifacts are complete. Please refer to the reviewer instructions and the FAQ for further information.

Review #2
By Bonino da Silva Santos submitted on 23/May/2025
Suggestion:
Accept
Review Comment:

I consider that the authors have adequately addressed the reviewers' comments and the paper is in a good state for publication.