Publications Office of the European Union (OP) developed model2owl to address the growing need for better semantic interoperability and data management within its systems.

model2owl enables semantic engineers to efficiently transform UML (Unified Modeling Language) models into OWL (Web Ontology Language) ontologies, SHACL shapes, model documentation, and other derivative artifacts. What began as a solution to a problem encountered in the eProcurement Ontology Project, developed by Meaningy for the European Publications Office, has become a powerful and essential tool for standardization initiatives across multiple sectors.
This blog delves into how model2owl facilitates semantic data specifications, its evolution in the eProcurement Ontology Project, the challenges it addresses, and its broader impact on public procurement and beyond.
1. What Is model2owl?
model2owl is a tool that assists semantic engineers and standardization initiatives in transforming and automating domain models into semantic data standards. It simplifies creating consistent and coherent models across various artifacts, eliminating the manual errors that often plague such processes.
1.1 Simplifying Complex Semantic Standards
At its core, model2owl helps simplify the complex task of creating consistent semantic data specifications by acting as a bridge between domain experts and technical experts.
- Automating Consistency Across Artifacts: When working with semantic data standards, a key challenge is ensuring that all derivative artifacts (such as UML diagrams, OWL ontologies, SHACL shapes, and documentation) are consistent. model2owl automates this process, ensuring that any change made in one artifact is propagated across all others.
- Serving Both Domain and Technical Experts: Domain experts understand the business side of things but may not have the technical expertise to create formal ontologies. model2owl bridges this gap by allowing domain experts to work with familiar UML diagrams while ensuring that the underlying technical aspects remain consistent and coherent.
This functionality is critical for ensuring the success of standardization initiatives. By ensuring that every component of a data specification reflects the same information, model2owl eliminates errors. It ensures all stakeholders, from domain experts to software developers, work with accurate, up-to-date information.
1.2 The Power of a Single Source of Truth
One of model2owl’s most powerful aspects is its ability to provide a “single source of truth” for all modeling activities. Any change made in one model representation is automatically reflected in all others, saving time and reducing the likelihood of errors.
- Centralizing Changes: Suppose a domain expert decides to change the label of a class from “organization” to “business entity.” Without model2owl, this change would need to be manually reflected in the UML diagram, the OWL ontology, the SHACL shape, and the associated documentation – a process that is both time-consuming and prone to errors. With model2owl, this change is made once, and the tool ensures that it is automatically updated across all relevant artifacts.
- Ensuring Consistency Across Representations: A semantic data specification is not just a UML diagram or an ontology – it’s a collection of artifacts that provide different perspectives on the same underlying information. model2owl ensures that these artifacts are always consistent with one another, eliminating the possibility of discrepancies between different representations of the same model.
This capability is especially important in standardization initiatives, where consistency ensures that the resulting data standards are accurate and reliable.
2. The Role of model2owl in the eProcurement Ontology Project
The eProcurement ontology project, an EU initiative to model public procurement data, was the original use case for model2owl. Over time, the tool evolved from a simple internal utility to a robust, widely adopted solution capable of automating complex processes and ensuring semantic consistency.
2.1 How the eProcurement Ontology Project Shaped model2owl
The eProcurement ontology project was initially focused on modeling data related to public procurement at the EU level. However, as the project grew to cover other subdomains, such as procurement evaluation, cataloging, awarding, and invoicing, it became clear that managing the complexity of these relationships was an enormous challenge.
- From OWL Ontologies to Full Semantic Data Specifications: Initially, the project focused on developing OWL ontologies to represent public procurement data. However, as the scope expanded, it became clear that the project required more than OWL files. It needed a full semantic data specification that included UML diagrams, SHACL shapes, and comprehensive documentation.
- Automating the Propagation of Changes Across Subdomains: Public procurement involves multiple interconnected subdomains, and a change in one subdomain can often affect others. Ensuring consistency across these subdomains was critical to the project’s success. model2owl made it possible to automate the propagation of changes, ensuring that all subdomains remained aligned.
The evolution of the eProcurement ontology project provided the perfect environment for model2owl to grow and prove its value as a tool for standardization. model2owl helped the project maintain consistency and accuracy across all its semantic data specifications by automating the transformation of UML models into OWL ontologies and other artifacts.
2.2 The Impact of model2owl on the eProcurement Project
The introduction of model2owl profoundly impacted the eProcurement Ontology Project, streamlining processes and eliminating errors that had previously slowed the development of semantic data standards.
- Reducing Errors and Increasing Consistency: Before model2owl, updating multiple artifacts manually often led to errors. For example, a class name might be updated in the UML diagram but not in the OWL ontology, leading to discrepancies. With model2owl, these errors were eliminated, as the tool ensured that any change made in one artifact was automatically reflected in all others.
- Speeding Up the Update Process: The time it took to implement changes in the eProcurement ontology project was significantly reduced. What once took months could now be completed in weeks, allowing the project to respond more quickly to changes and updates.
These improvements increased the efficiency of the eProcurement ontology project and ensured that the resulting data standards were more accurate and reliable. By automating the propagation of changes across artifacts, model2owl eliminated the risk of human error and made maintaining semantic data standards faster and more efficient.
3. Overcoming Challenges in Developing model2owl
While model2owl has proven to be a valuable tool for semantic data standardization, its development was not without challenges. The team behind model2owl had to overcome several technical hurdles, particularly related to the complexity of UML and the integration of open-source tools.
3.1 Addressing the Complexity and Ambiguity of UML
One of the biggest challenges in developing model2owl was dealing with UML’s inherent complexity and ambiguity. While UML is a widely used modeling language, it is also highly flexible, which can lead to inconsistencies in how models are interpreted.
- The Ambiguity of UML: UML allows for multiple ways to express the same concept, which can lead to ambiguity in how semantic models are represented. This ambiguity posed a challenge for the developers of model2owl, as they needed to ensure that the tool could generate accurate ontologies from UML diagrams.
- Creating Precise Interpretation Rules: To address this challenge, the team developed a set of precise interpretation rules for UML diagrams. These rules ensured that UML models were expressed consistently and unambiguously, making them easier to transform into OWL ontologies and other artifacts.
By establishing these interpretation rules, the team ensured that model2owl could generate accurate and consistent semantic models from UML diagrams, overcoming one of the biggest hurdles in the tool’s development.
3.2 Navigating the Use of Open-Source and Proprietary Tools
Another significant challenge in the development of model2owl was integrating open-source and proprietary tools. The European Commission strongly advocates using open-source tools to avoid vendor lock-in, but many UML editors are proprietary, requiring paid licenses.
- Balancing Open-Source and Proprietary Solutions: The development team needed to ensure that model2owl was compatible with open-source and proprietary tools, particularly when working with XMI (XML Metadata Interchange), the standard format for exchanging UML models. XMI is a complex format, and ensuring compatibility with it requires significant effort from the development team.
- Avoiding Vendor Lock-In: The push for open-source tools was important in developing model2owl. The team ensured that the tool could be used with open-source UML editors, making it accessible to a broader range of users and avoiding the need for costly proprietary licenses.
By ensuring that model2owl was compatible with open-source and proprietary tools, the development team could make the tool more flexible and accessible, overcoming one of the key challenges in its development.
4. The Growing Impact of model2owl Across Industries
While model2owl was initially developed for the eProcurement Ontology Project, its potential has since been recognized by other industries and standardization bodies. The tool’s ability to automate complex processes and ensure consistency across multiple artifacts has made it an invaluable asset in various domains.
4.1 Adoption by SEMIC and Other Standardization Bodies
One of the key milestones in the growth of model2owl was its adoption by SEMIC (Semantic Interoperability Community), an initiative by the European Commission to promote semantic interoperability across the EU.
- SEMIC’s Endorsement of model2owl: SEMIC recognized the need for a tool to standardize building semantic data specifications across the EU. They saw that model2owl provided a perfect solution for ensuring consistency and coherence across different semantic standards.
- Harmonizing Standards Across the EU: One of SEMIC’s key goals is to harmonize semantic standards across member states. By adopting the UML conventions implemented in model2owl, SEMIC could streamline the standardization process, ensuring that UML diagrams were drawn and interpreted consistently across different projects.
SEMIC’s endorsement of model2owl helped cement the tool’s reputation as a valuable asset for standardization efforts in the EU and across industries.
4.2 Expanding model2owl’s Capabilities
There is increasing demand to generate semantic artifacts like OWL and SHACL and technical artifacts such as XSD (XML Schema Definition) and JSON (JavaScript Object Notation) schemas.
- Meeting Developers’ Needs: While model2owl’s primary function is to generate semantic artifacts, developers are also growing in demand for the tool to generate technical artifacts. This would allow developers to take these artifacts and immediately start working with them without manually creating XSD or JSON schemas.
- Bridging the Gap Between Semantic and Technical Layers: One key challenge in expanding model2owl’s capabilities is ensuring the tool can bridge the gap between the semantic and technical layers. This requires careful coordination between semantic engineers and developers to ensure the generated technical artifacts are consistent with the underlying semantic models.
As model2owl continues to evolve, its ability to meet the needs of both semantic engineers and developers will be critical to its success. By expanding its capabilities to generate technical artifacts, model2owl has the potential to become an even more powerful tool for standardization initiatives.
Conclusion: model2owl’s Role in the Future of Semantic Standards
model2owl is a valuable tool in semantic data standards. Automating the transformation of UML models into OWL ontologies and other artifacts has eliminated errors, increased efficiency, and ensured consistency across multiple representations of semantic data specifications.
What started as a small project within the eProcurement Ontology Project has grown into a robust tool with the potential to improve how semantic data standards are created and maintained. As model2owl expands its capabilities and gains recognition from standardization bodies like SEMIC, it will play an increasingly important role in shaping the future of semantic data standards across industries.
model2owl is owned by the Publications Office of the European Union, and Meaningfy was honored to have contributed to the development of this project by providing semantic expertise and technical implementation. More information about the project here.

Meaningfy continues to support the European Commission’s initiatives, leading the charge toward a transparent, efficient, and interconnected European public sector. If you represent a European Institution or a public company that needs to implement an interoperability solution, contact us, and we’ll help you implement it effectively.