As sustainability, resource eficiency, and traceability gain increasing importance, concepts such as the Circular Economy (CE) and the Digital Product Passport (DPP) are becoming central to regulatory and industrial eforts. While sector-specific DPPs are being developed, their integration with existing standards and ontologies remains a key challenge. This paper examines how domain-specific ontologies and standards can be leveraged to enhance the semantic annotation of DPP data. We systematically compare existing Core DPP ontologies (C-DPPOs) and assess their adaptability to specific use cases. To illustrate the approach, we analyze two case studies: the car battery and the timber wall. These use cases represent diferent levels of regulatory and industry preparedness for DPP implementation. We identify relevant information sources (IS), including ontologies and standards, and evaluate their applicability for extending a Core DPP ontology. Our findings reveal substantial diferences in IS coverage depending on the maturity of EU-mandated DPP initiatives. Based on this analysis, we propose an initial selection of ISs that can efectively provide the required information. This study lays the foundation for linking C-DPPOs with domain-specific extensions, ensuring interoperability and facilitating the practical implementation of DPPs across industries.
The transition from a linear to a data-driven circular economy (CE) is one of the most pressing
challenges modern societies face. Especially in regions like the European Union (EU), where economic
stability relies on the import of finite resources, ensuring sustainable resource use has become a strategic
priority. However, the successful implementation of CE principles depends heavily on the collection,
storage, and exchange of comprehensive product data to enable informed decision-making by various
CE actors. In recent years, data spaces have emerged as decentralized infrastructures designed to
facilitate secure and sovereign data sharing by CE actors [
CEUR
ceur-ws.org machine-readable description of battery-related data and provides recommendations for ontology-based modeling. It identifies key domain-specific ontologies, such as the Battery Interface Ontology (BattInfo) and the Battery Value Chain (BVC) Ontology, which can be leveraged to annotate battery-related data in a standardized manner. These ontologies play a crucial role in aligning BPPs with existing regulatory and industry frameworks, ensuring seamless data integration across manufacturers, supply chain actors, and regulatory bodies.
However, while these recommendations represent a significant step toward a structured DPP
implementation for battery data, they also highlight the broader challenge of semantic interoperability, enabling
seamless data reusability and integration. The DPP and its DPP system [
Ontologies provide a structured way to address this challenge by defining a machine-readable
vocabulary that can be used to standardizes how product data is represented and exchanged. Thus, there is a
need for a Core DPP Ontology (C-DPPO) that acts as this common semantic foundation.
This is particularly valuable for the semantic description of data, as ontologies can serve as a common
ground for the semantic description of diferent data sets, enabling interoperable data exchange. This
eliminates misunderstandings caused by ambiguities inherent in raw data and provides clarity and
structure that facilitates the interpretation and analysis of newly acquired information. By leveraging
semantic modeling in combination with public ontologies and data spaces, we can ensure that DPP data
is structured in a way that aligns with the FAIR principles—making it findable, accessible, interoperable,
and reusable [
However, modeling a C-DPPO requires careful consideration of several key aspects. First, future DPPs
will be mandated across highly diverse industries, including textiles, electronics, and construction.
Hence, as highlighted by Jansen et al. [
In this work, we address two pressing key research questions, resulting from these considerations: (1) What is the current state of C-DPPOs? (2) Which ontologies and standards can already be used for modeling sector-specific DPP extensions? So far, DPP ontologies have primarily been designed as modular core ontologies, but their applicability across multiple use cases remains largely untested. To bridge this research gap, we analyze two distinct use cases that represent diferent stages of DPP standardization and development. This comparison highlights the varying challenges and starting points for DPP implementation across industries. The first use case focuses on car batteries and the aforementioned upcoming BPP. We choose this use case because it represents the most advanced stage of DPP development. With the introduction of the BPP, this sector is, as previously stated, subject to regulatory mandates, detailed industry standards, and ontology recommendations. Examining this case allows us to explore how existing frameworks can be leveraged to extend a C-DPPO. In contrast, the second use case, a timber wall, represents an industry without predefined DPP requirements from the EU. This sector features complex supply chains but ofers significant potential for improving data exchange and interoperability between stakeholders. Analyzing this case highlights the challenges industries face when no established ontological frameworks exist, emphasizing the need for structured sector-specific extensions.
To address these aspects, the paper is structured as follows: Section 2 provides an overview of existing DPP research and the state of C-DPPOs. Section 3 compares the available C-DPPOs. In Section 4, we present an analysis and classification of potential Information Sources (ISs) that can be used to extend a C-DPPO, while Section 5 applies these insights to the two selected use cases. Finally, we discuss our ifndings in Section 6 and conclude with key takeaways and future research directions in Section 7.
This section provides an overview of prior research on DPPs, focusing on regulatory developments, industry initiatives, and ontology-based modeling eforts. We start with an overview of general DPP research and end with our findings on available C-DPP ontologies. For this search, Google Scholar and ResearchGate were used to identify relevant academic papers.
Research on DPPs has gained momentum since the introduction of the concept in the ESPR [
A more structured approach is presented by Kebede et al. [
Both Jansen et al. [
In contrast, Kebede et al. [
Classification, End of Life Management, Standard, Environmental Impact, Manufacturer, Supply-Chain
Information Management and Use and Maintenance Modules. These modules are documented and
marked as a work in progress in the GitHub repository [
For this work, we focus on three key modules that require adaptation for sector-specific DPP extensions: (1) Material Module: While it defines a core ontology, each use case must extend it to capture domainspecific materials used in a product and throughout its lifecycle. A clear nomenclature is essential for precise semantic annotation. (2) Environmental Impact Module: Although it already defines several key environmental indicators, it is not exhaustive. Each use case must determine additional relevant indicators to assess environmental impact, circularity, and social sustainability. (3) Supply Chain Information Management Module: Each use case features a distinct supply chain with diferent manufacturers, suppliers, and logistics processes. Therefore, nomenclature and supply chain-specific metadata must be added.
These relationships are visually summarized in Figure 1a.
In the following, we examine how these three modules can be extended to fit specific use cases while
maintaining standardization and interoperability. We focus on the documents published by the EU DPP
initiatives and extend our search by searching Google Scholar, ResearchGate and Google.
Extending a C-DPPO involves a twofold challenge. The first step, as in standard ontology modeling,
is to define the IRs for a given use case and translate them into Competency Questions (CQs). As
defined by Grüninger et al. [
Once the CQs are defined, the second step is to structure the ontology extension in a way that maximizes standardization and interoperability. This can be achieved by using either widely used ontologies or open standards as ISs. In evaluating diferent IS candidates, suitable as extension, two key characteristics must be balanced to determine their suitability: the utility for ontology integration, i.e. how much further work someone has to do to integrate this IS into the ontology, and the level of acceptance of the IS, i.e. how many people have agreed on it. Figure 1b visualizes these characteristics. The closer an IS is to the top-right corner, the more suitable and relevant it is for integration into the DPP ontology network.
Ontologies and standards each ofer distinct advantages for extending a C-DPPO. Ontologies are
inherently structured and machine-readable, making them the easiest to integrate. In contrast, standards
are typically provided in written form, requiring manual translation into an ontology format. However,
standards contain agreed-upon nomenclature, which, when correctly utilized, ensures terminological
consistency and industry-wide acceptance [
Sector specific
Indicators Product Module
Material Module
Material specific ontology Environmental Impact (EI) Module Supply-Chain Information (SCI) Module Standard Module
End of Life Management
Module
Sectorspecific SC
(a) Own visualisation of the modules of the C-DPPO
presented by Kebede et al. [
This consideration leaves us with six potential sources of information that can be used to extend the C-DPPO (see Figure 1b (b)): The first source that should be preferred are EU defined and published ontologies, followed by EU recommended ontologies. The next three ISs are oficial EU standards (e.g. with the prefix EN, industry standards such as ISO, or DIN, and ontologies published by the scientific community. The choice of which IS to prioritize depends on the specific use case and the trade-of between ease of integration and level of acceptance (see Figure 1b (a)).
The last IS is the subjective opinion based on experience that should be used as a last resort if none of the previous sources are available.
In this section, we identify available ontologies that could be used to extend the C-DPPO for two selected use cases. We follow the approach outlined in Section 4, which includes defining CQs derived from use case-specific IRs and mapping relevant ISs to assess their suitability for answering these CQs.
Car batteries contain critical raw materials, such as lithium and cobalt, which are not mined domestically
in Europe. Given the high dependency on imports, the EU has prioritized sustainable and circular
strategies to secure supply chains and reduce environmental impact. This rationale led to the BPP.
Since the introduction of the Battery Regulation [
The Battery Regulation defines key IRs for the BPP. We focus on three representative ones—battery
materials, carbon footprint, and manufacturing activities—which require extending three C-DPPO
modules introduced in Section 4. These IRs are translated into the following competency questions
(CQs), with the relevant module that needs to be extended to answer this CQ noted in parentheses:
CQ1 What materials are used in the battery? (Material Module)
CQ2 What is the total battery carbon footprint of the battery throughout its lifetime? (EI Module)
CQ3 What are the manufacturers of the battery and what activities did they perform? (SCI Module)
In table 2, we present a non-exhaustive list of available ontologies and standards that can be used to
extend the C-DPPO for the Car Battery use case. It provides an overview whether they qualify as
ontologies, and whether they have been oficially published or recommended by the EU. Additionally,
the Module column maps the identified information sources (ISs) to the corresponding modules proposed
by Kebede et al. [
Before evaluating how these ISs can be used to answer competency questions one to three, it is
important to first examine the EU(-recommended) column. This column indicates which sources have
been published or recommended by the EU or EU-funded projects, helping to prioritize which ISs to
favor. AUTO has a check in parentheses because it was used and therefore recommended indirectly by
the EU-funded project called DATAPIPE at TU Delft, which developed a car BPP demo with an ontology
and incorporated AUTO [
The most important ontologies here are the previously mentioned BVC and BattInfo Ontology, as both
are recommended for the BPP application in the Battery Passport Technical Guidance [
Figure 2 visualizes an extension of the C-DPPO designed to address the three CQs. As outlined in Section 4, a key limitation of the Material Module in the existing C-DPPO is the lack of a detailed nomenclature for battery materials. BattInfo fills this gap by providing a structured classification of chemical substances. Specifically, it defines skos:prefLabel ”ChemicalSubstance”, with subclasses such as ChemicalCompound (e.g., lithium salts, cobalt oxides) and ElementalSubstance (e.g., lithium, graphite). These BattInfo classes can be mapped to the C-DPPO’s existing structure, aligning with the ChemicalEntity or MaterialComponent classes.
A similar gap exists in the SCI Module, particularly concerning battery manufacturing terminology (CQ3). BattInfo introduces skos:prefLabel ”Manufacturing” which includes subclasses such as LiquidElectrolyteCellManufacturing or BatteryModuleManufacturing. These classes can be mapped to the ProvenanceActivity class that is part of the Material Module and connects to the SCI Module from the C-DPPO to specify the type of manufacturing or activity that was performed. This extends the SCI Module and contributes to answering CQ3.
Unlike CQ1 and CQ3, CQ2 cannot be fully addressed by any of the listed ontologies. However, the Battery Regulation provides a detailed definition of the required nomenclature and outlines the necessary data and IRs for measuring this indicator. This information can be used to manually extend the C-DPPO to incorporate a structured representation of carbon footprint data.
While the C-DPPO already provides a general framework for carbon footprint assessment, it currently
lacks a precise unit definition. This gap can be addressed by extending the Unit class within the EI
Module to incorporate standardized measurement units. By defining carbon footprint values in ”kg of
carbon dioxide equivalent” for individual lifecycle stages and specifying the total footprint as ”kg of
carbon dioxide equivalent per kWh of total energy provided over the battery’s expected service life” [
A timber wall begins its lifecycle in forests and forestry operations, undergoing multiple stages of processing and transformation before it becomes part of a building. Unlike the car battery use case, which involves a complex combination of materials, a timber wall consists predominantly of wood, with adhesives as the only significant additional component. Despite its seemingly simple composition, defining a DPP ontology for this product presents several challenges. Key IRs include identifying all manufacturers and suppliers, mapping the processing stages the timber undergoes, specifying wood characteristics (species, density, strength class), and quantifying the global warming potential of both production and transportation [24]. These IRs translate into the following CQs with their corresponding C-DPPO Module that needs extending: CQ1 Which manufacturers and suppliers were involved in the production of the timber wall? (SCI
Module) CQ2 What are the diferent processing stages the timber has undergone? (SCI Module) CQ3 What type of wood (species, density, strength class) is used in the timber wall? (Material Module) CQ4 What is the global warming potential measured in CO2 equivalent (kg CO2 eq./m2 during the life-stages of the timber wall? (EI Module) To determine whether these CQs can be answered using existing ISs, we again compiled a non-exhaustive list of available ontologies and standards (see Table 3). An important asset for describing data related
IFC for BIM
to the supply chain of timber are the CBV and EPCIS Ontology, that define event data. However, despite their usefulness in structuring supply chain data, neither EPCIS nor CBV provide timber-specific nomenclature Relevant standards that could help bridge this semantic gap are the StanForD 2010 standard or the ELDAT standard. StanForD 2010 standardizes data communication during timber harvesting. Therefore, StanForD 2010 includes nomenclature about tree species such as oak, pine, birch, and spruce, which can be used to extend the material module and contribute in answering CQ3. However, because ELDAT also defines tree species, an alignment of both standards is necessary to avoid redundancy in the ontology. While StanForD 2010 primarily deals with harvesting data, ELDAT governs post-harvest data exchange across market actors — including forest owners, timber industries, and logistics providers. It provides a structured representation of processing stages, such as wood measurement, wood intake, and wood allocation. Hence, it can be incorporated into the C-DPPO as subclasses of the ProvenanceActivity class, similar to the car battery use case, thereby contributing to CQ2 and the SCI Module. CQ1 can also be at least partially answered by StanForD 2010 and ELDAT, as both define the nomenclature of key supply chain actors. StanForD 2010 introduces roles such as timber harvesting organizations, while ELDAT expands on the logistics chain, specifying actors like shippers and wood dealers. These classifications can be mapped to the C-DPPO’s Agent class that is connected to class Provenance Activity.
However, StanForD 2010 and ELDAT only cover the logistics chain up to the first buyer, leaving gaps in the representation of downstream processes, such as manufacturing and integration into construction projects. A potential candidate to extend the C-DPPO beyond this initial scope is the Industry Foundation Classes (IFC) standard, widely used in Building Information Modeling (BIM) [31]. IFC enables the classification of timber walls under the IfcWall class, which represents generic wall structures. A more detailed description can be achieved using the IfcMaterialLayerSetUsage class, which allows for the decomposition of a wall into distinct material layers. These IFC concepts provide a strong foundation, though further adaptation is needed to align with timber wall-specific materials and processing steps. For CQ4 (global warming potential measurement), no existing standard or ontology explicitly defines a methodology for assessing carbon footprint at diferent lifecycle stages. To bridge this gap, insights from academic research, such as Sultana et al. [24], can be leveraged to inform necessary extensions to the C-DPPO.
The release of EPCIS 2.0 introduced the ”how” dimension, which enables the integration of sensor metadata—such as temperature, humidity, and other environmental indicators—through the SensorMetadata class. This structure can be leveraged to incorporate lifecycle-based emissions data by aligning the C-DPPO’s Event class with EPCIS’s EPCISevent class. Furthermore, SensorMetadata and SensorElement can be utilized to capture carbon footprint data at various lifecycle stages.
To fully support CQ4, an additional subclass under the Quantity class in the C-DPPO is required to represent carbon footprint values. A standardized unit definition, such as kg CO₂-equivalent per square meter (kg CO₂-eq./m²), must also be introduced — following the same approach as in the car battery use case.
Another potential information source, the ATSO [25], appears to be a strong candidate for extending the C-DPPO. However, since this ontology is not publicly available, its direct integration remains uncertain. Figure 3 illustrates an extension of the C-DPPO for the timber wall use case, integrating the available IS where applicable.
This study examined early eforts toward developing a general and modular DPP ontology, designed as a top-level framework to serve as the foundation for future sector-specific DPP implementations. We identified the most advanced C-DPPO to date and analyzed which modules require further adaptation for diferent industry applications. To assess the feasibility of such extensions, we investigated existing ISs, including ontologies and standards, both EU-recommended and non-recommended. By selecting car batteries and timber walls as contrasting use cases, we demonstrated how the availability of standardization resources significantly impacts ontology development eforts. In doing so, our ifndings reveal a stark contrast between the two selected domains.
For car batteries, while no publicly available DPP ontology currently exists, the BPP initiative is already in an advanced phase, supported by EU recommendations, regulatory frameworks, and sector-specific ISs. The challenge in this case is not the lack of data standards, but rather ensuring their semantic alignment and integration into a coherent ontology before the 2027 mandate takes efect. However, the tight implementation timeline raises concerns about whether the necessary DPP infrastructure and interoperability mechanisms will be fully established in time.
In contrast, the timber wall use case presents a diferent set of challenges. Here, the absence of a standardized DPP framework translates into significant gaps in available ontologies. Although certain industry standards (e.g., StanForD 2010, ELDAT, IFC for BIM) provide valuable data models, they lack a unified semantic structure that would enable direct DPP integration. While this sector does not yet face an immediate regulatory deadline, the lack of a comprehensive ontology underscores the need for proactive standardization eforts before future DPP requirements emerge.
This work establishes an initial foundation for further refinement and development of sector-specific DPP ontologies, but several key challenges remain: (1) Systematic Collection of IRs: To ensure completeness and consistency, structured surveys and consultations with industry stakeholders, policymakers, and researchers are needed. A deeper understanding of sector-specific data needs will provide a clearer roadmap for ontology extensions. (2) Comprehensive Literature Review of ISs: A more exhaustive review of existing standards, ontologies, and regulatory guidelines will help identify additional resources that could support DPP ontology development. Crucially, inconsistencies between diferent ISs must be resolved to ensure semantic alignment. (3) Evaluation of Modular DPP Ontologies in Practice: The general DPP ontology framework, as discussed in Section 4, should be tested with real-world sector-specific data to assess its adaptability. This involves examining how well existing modular DPP structures (e.g., those proposed by Kebede et al.) align with practical use cases. (4) Practical Implementation and FAIR Data Integration: Beyond theoretical development, the practical applicability of the DPP ontology must be demonstrated. This can be achieved by integrating real-world data into distributed data spaces, ensuring compliance with FAIR principles. Such implementations would highlight the benefits of standardized ontologies, facilitating seamless data exchange across industries.
This study has demonstrated that the challenges in developing DPP ontologies vary significantly across industries and depend on the availability of standardized information sources. By analyzing two contrasting use cases — car batteries and timber walls — we outlined a path to addressing these challenges. A key takeaway is the necessity of establishing a unified framework that connects sectorspecific DPP ontologies within a coherent top-level ontology. To assess the feasibility of such an approach, we evaluated the two most advanced DPP ontologies currently available, comparing their completeness and applicability. Additionally, we examined information sources—including standards and ontologies—to determine how they could be used to extend a general Core DPP ontology in a standardized and interoperable manner.
The current state of Core DPP Ontologies is preliminary, with the modular framework by Kebede et al. ofering the most comprehensive foundation to date. Broader validation and sector-specific extensions remain necessary.
Our findings highlight a clear disparity in standardization readiness between diferent industries. Products that are already subject to future DPP regulations, such as car batteries, benefit from welldeveloped standards and structured data models. In contrast, industries without an EU-mandated DPP, such as the timber sector, lack an established framework, requiring significant efort to align existing standards, define IRs, and develop sector-specific ontologies.
Addressing these gaps will be essential for ensuring that DPP adoption expands beyond early regulatory mandates, ultimately supporting a broader transition toward structured, interoperable product data across industries.
During the preparation of this work, the author(s) used GPT-4o and DeepL Write in order to: Grammar and spelling check. After using these tool(s)/service(s), the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication’s content.
This work has been supported as part of the research project TimberConnect which is co-financed by the European Union and supported by the Ministry for the Environment, Nature Conservation and Transport of the State of North Rhine-Westphalia with funds from the EFRE/JTF program NRW (funding code: EFRE-20800276). //doi.org/10.1007/978-1-4020-6761-7_18. doi:10.1007/978- 1- 4020- 6761- 7_18. [15] EDM Council, The automotive ontology (auto), https://github.com/edmcouncil/auto, 2025. Accessed: 2025-02-03. [16] S. Stier, L. Gold, Battery Value Chain Ontology (BVCO), 2023. [17] S. Clark, J. Friis, T. Vegge, Battinfo: The ontology for the battery interface genome – materials acceleration platform (big-map), https://github.com/BIG-MAP/BattINFO, 2021. Accessed: 2025-0203. [18] M. Ismail, A. A. Fareedi, H. Nazir, Battalogy: Empowering battery data management through ontology-driven knowledge graph (2024). [19] V. Nebel, L. Beran, V. Königer, A. Haghipour, M. Mutz, A. Taranovskyy, D. Werth, V. Knoblauch, T. Kraus, Developing an ontology on battery production and characterization with the help of key use cases from battery research, Advanced Engineering Materials (2024). URL: https: //doi.org/10.1002/adem.202401540. doi:10.1002/adem.202401540. [20] E. Consortium, Battery domain ontology, https://github.com/emmo-repo/domain-battery, 2025.
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