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Anelia Kurteva
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Facilitating Circular Economy (CE)’s monitoring requires access to data from different systems and data spaces. Motivated by this, a number of organizations have established data sharing agreements in line with the European Interoperability Framework to facilitate technical, semantic, organisational, and legal interoperability. Each data space, however, may follow its own domain-specific semantics. While this supports data’s interoperability within the data space, it also poses a challenge in cases such as CE’s monitoring, which requires data from several data spaces to be accessed, combined and analyzed. Supporting findable, accessible, interoperable and reusable (FAIR) data sharing not only within but also between data spaces is key. Ontology alignment can help facilitate semantic interoperability across data spaces and support CE’s monitoring. Following this, we present an upper-ontology-based alignment approach to aid CE’s monitoring in practice. We showcase the implementation of the approach for aligning the FEDeRATED upper-level ontology for data sharing with the RePlanIT (electronics), BattINFO (batteries) ontologies and the Catena-X (cars) data model. As a result, the alignments can be used by parties interested in data sharing between the battery, electronics and car data spaces to generate data sharing agreements, define data access controls and ultimately monitor CE’s implementation. We also share lessons learned from the implementation of the approach and provide a discussion on future directions for semantic-enabled CE monitoring.
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Facilitating Circular Economy (CE)’s monitoring requires access to data from different systems and data spaces. Motivated by this, a number of organizations have established data sharing agreements in line with the European Interoperability Framework to facilitate technical, semantic, organisational, and legal interoperability. Each data space, however, may follow its own domain-specific semantics. While this supports data’s interoperability within the data space, it also poses a challenge in cases such as CE’s monitoring, which requires data from several data spaces to be accessed, combined and analyzed. Supporting findable, accessible, interoperable and reusable (FAIR) data sharing not only within but also between data spaces is key. Ontology alignment can help facilitate semantic interoperability across data spaces and support CE’s monitoring. Following this, we present an upper-ontology-based alignment approach to aid CE’s monitoring in practice. We showcase the implementation of the approach for aligning the FEDeRATED upper-level ontology for data sharing with the RePlanIT (electronics), BattINFO (batteries) ontologies and the Catena-X (cars) data model. As a result, the alignments can be used by parties interested in data sharing between the battery, electronics and car data spaces to generate data sharing agreements, define data access controls and ultimately monitor CE’s implementation. We also share lessons learned from the implementation of the approach and provide a discussion on future directions for semantic-enabled CE monitoring.
RePlanIT Ontology for Digital Product Passports of ICT
Laptops and Data Servers
Journal article
(2025)
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Anelia Kurteva, Carlo van der Valk, Kathleen McMahon, Alessandro Bozzon, Ruud Balkenende
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, boost the data economy and digital innovation, the European Union has introduced 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 ICT and materials data and tools to support its interpretation for decision-making, are present. Utilising 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.
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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, boost the data economy and digital innovation, the European Union has introduced 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 ICT and materials data and tools to support its interpretation for decision-making, are present. Utilising 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.
The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies.
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The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies.
What is in Your Cookie Box?
Explaining Ingredients of Web Cookies with Knowledge Graphs
Journal article
(2023)
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Geni Bushati, Sven Carsten Rasmusen, A.K. Kurteva, Anurag Vats, Petraq Nako, Anna Fensel
The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies
unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie "disappears", and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine- readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation tool helps increase users’ awareness of cookies and data sharing. ...
unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie "disappears", and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine- readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation tool helps increase users’ awareness of cookies and data sharing. ...
The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies
unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie "disappears", and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine- readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation tool helps increase users’ awareness of cookies and data sharing.
unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie "disappears", and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine- readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation tool helps increase users’ awareness of cookies and data sharing.
Protecting and preserving individuals’ personal data is a legal obligation set out by the European Union’s General Data Protection Regulation (GDPR). However, the process of implementing data governance to support that, in a decentralised ecosystem, is still vague. Motivated by the need for lawful decentralised data processing, this paper outlines several relevant questions from legal, privacy and technology standpoints that need to be considered
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Protecting and preserving individuals’ personal data is a legal obligation set out by the European Union’s General Data Protection Regulation (GDPR). However, the process of implementing data governance to support that, in a decentralised ecosystem, is still vague. Motivated by the need for lawful decentralised data processing, this paper outlines several relevant questions from legal, privacy and technology standpoints that need to be considered
Journal article
(2023)
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A.K. Kurteva, Tek Raj Chhetri, Amar Tauqeer, Rainer Hilscher, Anna Fensel, Kevin Nagorny, Ana Correia, Albert Zilverberg, Stefan Schstakov, More authors...
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.
...
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.