In a previous blogpost by my colleague Wouter (ENDORSE '25, BOMOS), he talked about our vision of using Generative AI for semantic interoperability. The core idea he conveyed there, and which I will also highlight here again, is that semantic interoperability at its core is not a technical problem. It's a problem of – a lack of – human coordination. Keeping this in mind, we have asked ourselves the question: How can AI help advance or accelerate the systematic BOMOS approach to standardization? In this blog post, I'm happy to show to you the first part of our STH Co-pilot roadmap: AI-assisted message model creation!

AI-assisted message model generation​

When creating a message model from the ground up, it's sometimes hard to just get started. That's where the AI-assisted generation feature can help. It can provide initial suggestions for message model elements, helping to kickstart the development process. You only provide a description for the message model you'd like:

Now the AI will generate a message model based on your description. The generated message model will be displayed in the editor, allowing you to review and refine it further:

Now of course this message model will not be perfect – far from it. In fact, it's likely to contain errors and omissions, but it's a starting point that can be refined and improved upon. Semantic Treehouse already has many great collaboration features, such as issues, comments, and support for iterative versioning.

Looking ahead​

Semantic Treehouse's AI-assisted message model generation feature is just one example of how AI can enhance standardization, collaboration, and productivity. As we continue to explore the possibilities of AI in software development, we'll be sharing more insights and best practices. Stay tuned for more updates on this exciting topic! In the meantime, if you'd like to become involved, you can join our Discord server and discuss your vision with our team. On our Gitlab page, you can follow the technical developments more closely as well.

That's why I'm very happy to share that we have reached a new milestone this month: mappings are now first-class citizens in Semantic Treehouse.

Why did it take so long?​

Supporting mappings has been a long held wish but they were seldom the highest priority. The exception for us has been our work on e-invoicing standards. In that domain, mappings between the European semantic data model (EN16931) and common syntaxes like UBL and UN/CEFACT CII became part of the mandatory European norm. This meant that mapping information needed to be documented, published, and presented in a web-friendly/user-friendly way. Also, practitioners working with e-invoices were accustomed to thinking in terms of UBL or UN/CEFACT CII fields, so helping them relate these familiar structures to their abstracted counterparts in the semantic data model was important for adoption and understanding.

As a result, years ago we created a kind of implicit support for mappings based on this need. Here's an example, with the semantic data model on the left and the UBL invoice syntax to the right:

The tree-based mapping view works well for e-invoicing syntax bindings, where mappings are relatively fixed and the highlighting clearly shows "it's over here!"

I think this exception proved the rule: mappings matter most when they bridge the gap between abstract semantic models and the concrete formats people actually work with.

What are mappings, exactly?​

Let's clarify what we mean by "mappings". We follow definitions from the semantic interoperability literature, particularly Jérôme Euzenat and Pavel Shvaiko's work and CROW's mapping whitepaper:

A mapping is the oriented, or directed, version of an alignment: it maps the entities of one data model to at most one entity of another data model. A mapping can be seen as a collection of mapping rules all oriented in the same direction—from one specification to another—with each element of the source appearing at most once.

Mappings help you answer questions like:

• "How does my organization's internal data model relate to the industry standard?"
• "Which fields in format A correspond to which elements in format B?"
• "How can I transform data from one representation to another?"

These questions are fundamental to achieving interoperability. We see this with our Dutch partners in sector initiatives implementing BOMOS. These are usually mature networks with high trust, shared business contexts, and familiar legal frameworks. But mappings become even more critical in geographically distributed or less mature networks, like many European Data Spaces. Where convergence to a single shared model is further away or less likely, explicit mapping support can be a catalyst for practical interoperability.

What does "first-class citizen" mean?​

Basically this means mappings are no longer hidden attachments to the data models, but actively managed objects that are just as important as the data models themselves.

In software design, a first-class citizen is an entity that supports all the operations generally available to other entities in that system. For Semantic Treehouse, this means that mappings have become a full specification type with all the capabilities you'd expect:

• Issue tracking: create and link issues to specific mapping rules, just like you can with message model elements
• Version control: maintain different versions of mapping specifications with versioning history
• Project integration: mappings appear in the specification index and belong to projects, so that they're discoverable and manageable
• Access control: role-based permissions apply to mappings, including group-level access control
• Import/Export: mappings can be imported and exported in standard formats for portability and reuse

Why now?​

Two developments converged to make this the right moment for fully integrating mapping support:

1. Backend refactor complete​

Our recent backend refactor has streamlined how we handle different specification types. Where implementing mapping support would previously have been a complex, multi-month project, it became straightforward and fast. What's exciting (at least to me!) is that this means any extension, addition, or change to STH is now less costly and time-consuming. This lets us set higher goals! We can be more ambitious about what we commit to in projects and more responsive to our communities' needs (we're already looking at process specifications next).

2. AI and semantic interoperability​

Recent developments in AI have created new possibilities for mapping creation and curation. We want to explore what AI can mean for reducing the manual effort of creating and maintaining mappings.

Since Semantic Treehouse serves as our living lab, we leverage internal R&D projects to explore emerging technologies and test their practical value. Through our recent HAVOC research project we learned that reliable AI mapping requires a 'human-in-the-loop' workflow. This insight led us to develop this new mapping infrastructure in STH, because we needed an environment where AI can suggest mappings, but where experts can validate and manage them with some confidence.

Real-world impact: Digital Product Passports in CIRPASS-2​

The practical value of mapping specifications became immediately clear in our work with the CIRPASS-2 project, a Horizon Europe initiative developing Digital Product Passport solutions across multiple industries.

The scenario is common in European standardization work: industry pilots develop practical, bottom-up data models based on their specific needs, while parallel efforts create top-level, cross-sectoral ontologies. The challenge is bridging these two worlds.

In the textile DPP pilot, partners including Kezzler, Atma.io (Avery Dennison), and TripleR collaboratively developed a shared data model containing approx. 15 key data attributes. This started as an Excel sheet, which is a common starting point for these kind of project consortia. We imported this into Semantic Treehouse as a formal message model specification.

The next step was mapping their model to the official CIRPASS-2 core DPP ontology. When we demonstrated the mapping functionality during a follow-up meeting, they got it right away. They confirmed that visualizing the connection between their practical Excel models and the abstract ontology was exactly the missing link they needed to make the two converge.

What we showed looked something like this: The mapping rules are now explicit (on the left), with source and target specifications side-by-side and detailed mapping information in the center.

Both viewing approaches remain available in STH because they still serve different needs. The simpler view without explicitely listing the mapping rules works well for fixed syntax bindings like e-invoicing, since in that case the mapping is established and not subject to much discussion. The new view better supports scenarios where mappings are being collaboratively developed and require more detailed justification and discussion.

With mapping specifications in STH, these consortia can now:

1. Connect industry-specific models with cross-sectoral ontologies
2. Share mappings among pilot partners and other stakeholders
3. Version and maintain mappings as both industry models and EU standards evolve
4. Generate transformation specifications for actual data conversion and testing
5. Show how sector standards align with regulatory requirements

While CIRPASS-2 is an EU project, the same 'bottom-up meets top-down' challenge exists for national and sectorial initiatives, where software vendors need to map industry standards to their own (API) implementations. We call these "implementation profiles". More about that soon.

What's next?​

For our Dutch sector communities using STH, like Ketenstandaard, SETU and SUTC, this means better supprt for one of the most-requested features. For our European partners in data space initiatives, this underscores STH's position as a mature vocabulary hub implementation that is capable of handling the heterogeneity that is typical for federated data spaces.

Of course the work is far from done. As with any new capability in our living lab, we're already identifying improvements and refinements. We work as an agile team and have to balance consultancy, research, and platform development in a project-based organisation (TNO). This means features are development further where our projects and communities need them most. But we've spotted several UX improvements we'll be working through in any case.

We're eager to see how communities will use mapping specifications to solve their interoperability challenges, and we welcome your feedback as we continue developing this functionality.

Join the conversation on Discord or Gitlab.

That was the subject I explored in my recent presentation, "Practical Experiences with BOMOS, Semantic Treehouse, and GenAI," at the ENDORSE 2025 conference in Brussels. For those who couldn't attend, I'm happy to share the full recording of the session.

Overview​

This 15-minute talk provides an overview of our vision and practical examples so far in the field of interoperability and AI. It has three parts:

1. Foundation: the presentation starts by framing interoperability through our socio-technical lens. I introduce the BOMOS framework as our method for organizing the "participatory" work of standardization, and Semantic Treehouse as the digital platform built to support these collaborative processes and the key roles involved.
2. Demos for two standardization roles: the core of the talk showcases two proofs-of-concept, each designed to assist a specific role in the standardization process:
   1. For the domain expert: a demo of the AI Specification Generator, designed to kickstart the development of a new standard. In my demo I used a prompt for a "Digital Product Passport for concrete" and it quickly creates a complete, structured data model within the platform.
   2. For the functional manager: a demo of the AI Issue Co-Pilot, which assists knowledge managers in handling complex community feedback like issues, questions or new requirements. The assistant can use the rich context already within Semantic Treehouse (like specifications, previous issues, working group notes) to provide analysis and suggest next actions.
3. Research agenda: finally, I challenge myself and others with the danger of the "horseless carriage syndrome", i.e. are we using AI to optimize old workflows? After which I summarize our research agenda:
   1. more advanced AI-augmented standardization processes
   2. autonomous data integration and negotiation
   3. and enabling evidence-based standards evolution through observability in data spaces.

I'm thankful for the energy and ideas from the other attendees and speakers.

Looking back, the experience reinforced my belief that we are on the right track.

Join the Conversation​

Conversations about AI's role in standardization are still just beginning. I believe the best solutions will come from open dialogue and community collaboration.

So, watch the full presentation above and please let us know what you think. You can join the discussion with our team and other community members on our Discord server or follow our progress on these AI features on our public roadmap. Or just send me an old-fashioned email ;)

Terugblik​

Dit was inmiddels de derde keer dat we een dergelijke sessie organiseerden. In 2022 hielden we onze eerste gebruikersmiddag, en deze keer kwamen we weer samen bij YouMeet in Utrecht voor een middag vol inspiratie, uitwisseling en gezamenlijke roadmapping.

Het doel van deze sessie was opnieuw tweeledig. TNO wilde samen met standaardisatie-experts uit verschillende sectoren:

1. Kennis en ervaringen delen over trends en ontwikkelingen op het gebied van digitalisering en interoperabiliteit
2. Samen de toekomstige ontwikkelroadmap van Semantic Treehouse vormgeven en prioriteren

Trends en ontwikkelingen​

De sessie begon met een interactieve kennismaking waarbij we vroegen: welke trends en ontwikkelingen zien jullie? Het gesprek dat volgde was levendig en veelzijdig. Niet verrassend stond AI bovenaan de lijst - van "Wat moeten we met AI?" tot concrete toepassingen zoals AI in combinatie met knowledge graphs en AI voor het creëren van knowledge graphs.

Andere belangrijke thema's die naar voren kwamen:

• Data spaces en de groeiende behoefte aan data-uitwisseling
• Digital Twins als belangrijke toepassing
• Mappings blijven cruciaal door de variëteit aan formaten in de markt
• Regelgeving als drijfveer (zoals de Data Act en CSRD)
• Soevereiniteit en de afhankelijkheid van Big Tech
• Federatief data delen en het creëren van de juiste "arena" voor samenwerking

Highlights 2024-2025​

Michiel Stornebrink, product owner van Semantic Treehouse, presenteerde de belangrijkste ontwikkelingen van het afgelopen jaar. Tien hoogtepunten werden uitgelicht:

1. JSON validator - volledig geïmplementeerd in het platform
2. SHACL validator - RDF validatie toegevoegd
3. Volledig open source - codebase én issues nu publiek toegankelijk
4. Content-based access control - fijnmazige controle over rollen en toegang
5. Notificaties systeem - houdt discussies over issues levendig
6. Data Model Canvas - verbeterde interface voor bekijken en vergelijken van specificaties
7. Profile designing - profielen maken op basis van basisstandaarden
8. Bottom-up approach - applicatieprofielen genereren vanuit voorbeelddata
9. Digtale product passpoorten - het leggen van de link tussen regelgeving, requirements en standaarden
10. Vocabulary hub federatie - eerste stappen naar federeren van publicatie van data modellen

Roadmap: wat komt er aan?​

De roadmap bestaat uit drie sporen:

• Hoog TRL (Technology Readiness Level) - wat gebruikers nu nodig hebben
• R&D spoor - toekomstige mogelijkheden en innovaties
• Proces & Betrokkenheid - continue verbetering van platform en community

Drie grote highlights voor dit jaar:

1. Semantic Treehouse v4 - complete backend refactor
2. Nieuwe specificatietypes - data requirements, procesmodellen en mappings als first-class citizens
3. AI Copilot - eerste implementatie van AI-ondersteuning

AI visie en demo's​

Een belangrijk deel van de sessie was gewijd aan onze visie op AI en semantische interoperabiliteit. We positioneren AI niet als bedreiging, maar als krachtig hulpmiddel. Met een tempel-metafoor illustreerden we hoe AI twee pilaren kan ondersteunen: beheerders (sneller issues verwerken, conceptmodellen opstellen) en gebruikers (next-level implementatiesupport, proactieve compliance monitoring).

Onder het motto "Show, don't tell" lieten we twee live demo's zien:

• Semantic Treehouse Copilot - AI-assistentie bij het verwerken van complexe issues
• Helpdesk Agent - geautomatiseerde ondersteuning voor gebruikersvragen

Gezamenlijk roadmappen​

Het hoogtepunt van de middag was het gezamenlijke roadmapproces. We tekenden de roadmap na op een groot whiteboard en gaven elke deelnemer speelgeld (briefjes van €5, €10, €20 en €50) om hun prioriteiten aan te geven.

De community-gedreven prioritering leverde duidelijke winnaar op:

Top 3 prioriteiten:​

1. Proces specificaties - €170k (met stip op één!)

   • Nieuw specificatietype voor data exchange processen: eerst proces definiëren, dan benodigde berichten creëren
   • Helpt belangrijke context van berichtmodellen te documenteren. We beginnen met visualisatie van interaction patterns via simpele sequence diagrams (zoals Mermaid)
   • Technisch gesproken biedt dit kansen voor betere OpenAPI generatie voor complete API's in plaats van losse endpoints

2. Vocabulary Hub Federation - €100k

   • Netwerken van gefedereerde vocabulary hubs: vindbaar en herbruikbaar maken van datamodellen over platforms/gemeenschappen heen
   • Wat nu al werkt: DCAT-exports voor catalogus-uitwisseling tussen platforms

3. Semantic Interoperability Analytics - €60k

   • Evidence-based insights voor beheerorganisaties: het idee is om 'observability' ontwikkelingen in data spaces te gebruiken voor data over gebruik van standaarden
   • Metrics zoals usage frequency van infirmatie elementen, validation errors en adoption rates
   • "Predictive maintenance" voor semantische standaarden
   • "Heat map" over een standaard heen die fequentie/intensiteit van gebruik van de verschillende onderdelen visualiseert.

Andere belangrijke items:​

• Mappings: first-class citizen ondersteuning
• Lineage visualisatie: transparantie over ontologie-herkomst
• AI features: interessant genoeg relatief weinig stemmen

Een paradox en reflectie​

Opvallend was dat ondanks het uitgebreide en enthousiaste gesprek over AI, de AI-gerelateerde roadmap items relatief weinig stemmen kregen. De verklaring die we hoorden was veelzeggend: "AI is een middel, niet een doel. De doelen die we belangrijk vinden, die kun je waarschijnlijk met AI wel makkelijker bereiken."

Het is een beetje ondeugend maar ik moest denken aan die (overigens waarschijnlijk neppe) quote van Henry Ford: "If I had asked people what they wanted, they would have said faster horses." Want is dit nou een nuchtere houding naar AI of een blijk van inside the box denken? Het bevestigt in elk geval mijn overtuiging dat TNO en andere innovatieorganisaties moeten blijven zoeken naar de grote voordelen die AI mogelijk maakt, ook als de directe vraag er nog niet is.

Vervolg​

We sloten af met een oproep voor ons AI-programma dat 1 september van start gaat. Samen willen we leren hoe we meer kunnen bereiken met AI op het gebied van interoperabiliteit en standaardisatie. Geïnteresseerde organisaties kunnen contact opnemen voor een persoonlijk gesprek over deelname.

Het resultaat van deze middag is opnieuw een gezamenlijke roadmap die beter is afgestemd op de behoeften van onze community. De duidelijke prioritering geeft ons een sterke motivatie om deze functionaliteit zo snel mogelijk te realiseren.

Dankwoord​

Onze dank gaat uit naar alle deelnemers: SETU, Ketenstandaard, NEN, Geonovum, Logius, NimbleNova, SUTC van Transport en Logistiek Nederland, en alle andere aanwezigen. Jullie inbreng en enthousiasme maken Semantic Treehouse tot wat het is.

Wilt u de volgende keer ook deelnemen? Houd onze communicatie in de gaten want we plannen deze sessies nu jaarlijks. Tot dan: blijf innoveren in de wereld van semantische interoperabiliteit!

For many organizations in the Dutch standardization community, the BOMOS (Management and Development Model for Open Standards) provides the framework for this coordination. BOMOS guides the creation and maintenance of high-quality, open standards through structured processes covering strategy, tactics, operations, implementation support, and communication.

This post clarifies how Semantic Treehouse serves as a practical, digital environment equipped with features specifically designed to support these BOMOS activities. Our platform was born from the need to execute these very processes more effectively, and this connection remains core to our identity, even as we serve broader roles like being a Vocabulary Hub in European Data Spaces.

Organizations like SETU (Dutch flexible staffing industry), Ketenstandaard (construction and technology supply chains), and SUTC (transport and logistics) are already using STH to implement BOMOS activities in their standardization work. You can explore all our STH communities to see how different sectors apply these approaches.

For readers unfamiliar with BOMOS: It's the Dutch framework guiding the lifecycle of open standards development and management, aiming for quality, sustainability, and interoperability. You can learn more from the official English BOMOS specification:

• Part 1: BOMOS Foundation.
• Part 2: BOMOS Elaboration

Mapping STH features to BOMOS activities​

Let's dive into how STH functionalities align with the core BOMOS activity layers:

Operational activities​

The practical activities that lead to new versions of standards.

• Initiation: BOMOS initiation involves identifying new ideas for specific specifications and organizing their successful development through analysis of interests, business cases, and agenda setting. Though this might seem strategic, BOMOS classifies it as operational because it focuses on practical work of starting specific standardization efforts within an established strategic framework. STH's Wizard supports the exploratory part of this activity through its bottom-up approach: users can upload sample data to quickly prototype what envisioned data models could look like, or leverage existing ontologies while easily adding extensions as placeholder elements. This enables rapid exploration of standardization ideas before formal processes begin.
• Wishes and Requirements: BOMOS describes this as "perhaps the most important step": gathering wishes and requirements from the community for both new standards and or new versions. The principle is that everyone should be able to submit wishes to increase support. STH's Issues feature directly supports this ongoing process by providing a digital environment where users can submit change requests and ideas, directly linked to specific specification elements.
• Development: BOMOS development involves substantive elaboration of solutions at conceptual level, keeping solutions separate from technologies for later implementation. STH's Wizard supports this by enabling maintainers to develop application profiles (message models) based on underlying semantic models (ontologies, schemas), maintaining the conceptual-technical separation BOMOS advocates.
• Implementation: BOMOS implementation involves actual adaptations to specifications and technical structures based on conceptual solutions. STH directly supports this through schema generation that produces concrete technical artefacts (XSD, JSON Schema, OpenAPI specs, RML mappings) from conceptual models.
• Documentation: BOMOS documentation requires appropriate records of management processes, including specification availability and historical overview of change requests. STH acts as the central documentation hub with tree views, versioning, and the ability to attach related documents directly to specific versions.

Tactical activities​

Activities that ensure stability in the medium term.

• Community: BOMOS focuses on openness through Krechmer's 10 Requirements for Open Standards. This means inclusive participation, consensus decisions, and transparent processes, among other things. STH's specific focus is to be a community platform and support this with Community management tools that keep track of the standardization community and handle the relationships between people, organizations, and groups within it:
  • Organization management,
  • Group management for organizing working groups,
  • Account management,
  • Roles & permissions for access control
• Architecture: BOMOS architecture addresses the stratification and interdependency of standards by helping users manage relationships between technical and semantic standards at different layers. For example, viewing a codelist also gives the user a list of specifications where this codelist is applied. Another example is the Wizard, which helps manage the interdependency between standards by letting you work with semantic models (conceptual layer) while generating concrete message specifications (technical layer) and maintaining clear relationships between them. STH also helps by managing core semantic assets like Ontologies and Taxonomies. We're working on Mapping specifications and Process specifcaitons to make support with architecture concerns even better.
• Quality Policy & Benchmarking: BOMOS quality policy uses a three-dimensional model measuring product quality, process quality, and quality in practice to ensure standards achieve their fitness-for-use goals. STH supports this comprehensive approach: The Wizard improves standard quality by enabling maintainers to derive all message models from a single ontology, eliminating inconsistencies like having 9 different definitions of "Address" across specifications. Specification versioning and collaborative development further ensure standard quality, while the Validator enables implementation quality through self-service schema and business rule checking (Schematron, JSON Schema, SHACL validation).
• Rights Policy: BOMOS rights policy ensures intellectual property rights are managed to maximize openness through royalty-free, irrevocable licensing while protecting contributions. While STH doesn't set IPR policies itself, its controlled access and versioning features support the implementation of chosen rights policies, ensuring proper attribution and access control according to organizational requirements.
• Adoption & Recognition: BOMOS adoption strategy involves systematic promotion of standard uptake through financial tools, communication tools, and legal tools, focusing on removing barriers to implementation. STH supports this through its central, accessible platform with clear documentation, examples, and validation tools that significantly lower technical barriers to adoption, while Groups facilitate the community building essential for successful adoption.

Implementation support​

• Validation & Certification: The Validator is the primary tool, allowing self-service checking for implementers. STH's validator API enables some communities to organize certification programs, as demonstrated by Ketenstandaard, where community members receive certificates visible on an industry platform (in this case at datakwaliteit.nu) after a validation process.
• Generated Artefacts (schemas, examples, OpenAPI specs) provide ready-to-use technical components that reduce implementation burden and errors.
• Helpdesk: The Issues system functions as a community helpdesk where implementers can seek assistance and clarification. As issues are directly linked to specification elements, the community members can easily see which issues other community members have submitted. These kinds of overviews also help the maintainers of the standards in checking for duplicates and related issues. Other STH features reduce strain on helpdesk support by providing self-service capabilities: clear documentation, validation tools, and generated examples enable users to find answers independently, reducing the volume of basic implementation questions.

Communication​

• Publication: BOMOS emphasizes that modern standards require online documentation beyond PDFs, with web environments that support the development process itself. STH exemplifies this approach as the primary publication channel for standards managed within it, providing online documentation with tree views, version management at the element level, and direct linking of implementation examples to specification sections.
• Promotion: BOMOS promotion involves building communities and targeting different audiences (business stakeholders vs. technical implementers) with tailored messaging. STH supports this through highlighted specifications, clear project overviews, and community features like Groups, though broader marketing campaigns remain the responsibility of individual standardization organizations.
• Complaint Handling: BOMOS complaint handling addresses procedural concerns about the standardization process itself - fairness of decision-making, governance disputes, or appeals about standardization procedures. This requires formal governance structures outside STH's scope. The Issues system handles technical implementation feedback, not process complaints.

Strategic activities​

Long-term direction

• Governance: BOMOS governance involves creating institutional arrangements - defining legal structures, decision-making processes, governing bodies, and organizational mandates. While STH provides roles and permissions to implement governance decisions, the strategic governance challenge of designing these institutional arrangements lies outside STH's scope.
• Vision: BOMOS vision involves developing substantive long-term direction and innovation roadmaps - "the spot on the horizon" for standards development. This strategic activity is conducted outside STH, though the platform provides means to implement and communicate the resulting vision through managed standards.
• Financing: BOMOS financing requires developing sustainable financial models and revenue streams to guarantee long-term funding aligned with demand. This strategic activity is entirely outside STH's scope, though STH provides the professional platform environment that supports justification for structural funding.

The future: AI in semantic standardization?​

BOMOS and its pillars already go a long way back, but their relevance for successfully tackling interoperability challenges stands in the changing landscape of data and AI. The Semantic Treehouse team is an ambassador of BOMOS and knowledgeably implements its core features to support standardization organizations and communities.

To keep up with the data and AI developments, the Semantic Treehouse team is currently researching and developing a new direction in BOMOS-aligned standardization.

Our STH Co-pilot roadmap feature represents this new direction. Think of AI-assisted issue coordination, message model creation and automated mapping suggestions. We're exploring how artificial intelligence can enhance the structured processes BOMOS defines, in particular the operational activities like development and implementation. Given what AI brings to the table, the question is how AI can support the systematic approach to open standards development that BOMOS advocates, while maintaining the quality and consensus principles that make standards truly work.

Get involved​

For BOMOS practitioners: We'd love to hear from standardization professionals familiar with BOMOS. How does your organization currently implement these activities? Are there gaps where STH could better support your BOMOS processes?

For European partners: If you're working with semantic standards in data spaces or digital initiatives, we're happy to discuss how STH can support your specific standardization challenges.

Learn more about BOMOS: Explore the full English BOMOS specification to understand the complete framework for open standards development.

Join our community: Continue this conversation on our Discord server or explore what's coming next on our roadmap.

Semantic Treehouse is developed by TNO and serves as a vocabulary hub in European data spaces. Learn more about our communities and upcoming features.

The need for practical implementations of DPPs is precisely what we are addressing within our work, and we are addressing it from multiple perspectives. For instance, we have established a DPP Semantic Treehouse environment – https://dpp.vocabulary-hub.eu/ – which serves as the hub where the 'common language' for all product types can be hosted, managed, and shared. Beyond this environment, we are actively engaged in various projects, developing features to simplify the process of creating DPPs. But how does that work? Let's dive into it!

From legislation to digital product passports and back​

Semantic Treehouse is supporting DPPs from start to finish, beginning with legislation. Legislation, such as the ESPR, describes the requirements that passports must meet. Based on these requirements, semantic models (that is, 'common language') are developed to ensure we all have a shared understanding and know what information needs to be included in a DPP. However, there will not be a single language in which all product types are specified. Instead, there will be multiple models for different product types, which we call DPP templates.

DPP templates are compliant with legislation, and can be seen as the instructions to create your DPP for a certain product. Once created, you are also able to validate your DPP against both the template itself and the originating legislative requirements. Semantic Treehouse supports this entire flow, from legislation to a validated digital product passport, as depicted in the figure below.

The four layers in this image are from the European Interoperability Framework

How does it work?​

1. Requirements: it starts with the requirements from legislation (e.g., ESPR), sector agreements, or specific business objectives. Together they form the DPP requirements, explaining exactly what information needs to be in the DPP. Semantic Treehouse can publish and visualize these requirements, such as:

   

2. Development of the common language by defining the basic concepts and the meaning (semantics) of the required data. This forms the "common language" that everyone understands. In projects like CIRPASS-2 and within JTC standardization, work is being done to formalize and store this as an ontology and publish it in Semantic Treehouse. For example:

   

3. Development of data models (DPP Templates): with the analyzed requirements and the developed ontology, Semantic Treehouse facilitates the development of standardized data models for various product types. These are the concrete "DPP templates" that precisely define which data elements are needed for each product type.

   What the creation of such data models allows is for organizations to easily see what information is expected in the interactions between IT systems about the digital product passport for each specific product. Since these templates are based on legislation or sector requirements, implementing the data models in the IT systems is a way to ensure compliancy.

   Below, you'll find an example of an Electric Bike Battery, based on the specified ontologies and data exchange requirements:

   

4. Management in Semantic Treehouse: the compliant data models (DPP templates) are published and managed in Semantic Treehouse by standardization organizations or individual organizations. Here, they are accessible to all relevant parties, including version control and governance to keep them current and reliable.

   

5. Creation of your digital product passport: manufacturers (or their software vendors) use these standardized DPP templates as a basis for implementation of their specific DPPs data exchange. They fill the template with their product data, so to speak.

   The figure below shows an example digital product passport of an electric bike battery in JSON-LD:

   

6. Validation with the DPP Validator: It is crucial that a digital product passport can be checked for validity and compliance; this can be done with our new DPP Validator! This validator checks whether the passport meets the specifications of the underlying data model (DPP template) and thus the set requirements. This ensures quality and interoperability. You can validate your digital product passport in any syntax, such as JSON-LD, as depicted below.

   

7. Use and Exchange: Approved and validated DPPs can then be confidently published, exchanged, and used by consumers and companies in the supply chain.

Looking forward​

What's the road ahead for DPPs? We see them as practical tools that boost transparency and business value, not just as regulatory tasks. However, there are still some challenges, which we address:

1. Access any DPP template: finding the right DPP template, in the right language, needs to be straightforward. This means enabling different systems, including Semantic Treehouse, to interoperate using open standards so users can easily retrieve any template for their products.
2. Ownership of standards: who maintains the DPP standards for each product type? While European standardization initiatives are progressing, industries themselves must take an active role in governing and creating the standards relevant to their sectors.

We focus on making DPP adoption simpler with Semantic Treehouse. You can follow our progress and upcoming developments on our public Semantic Treehouse development roadmap. Our aim is to provide practical solutions to implement DPPs.

Both the construction and logistics sector have identified the paperless bulk transport use case as a shared problem. Therefore a collaboration is set up between Ketenstandaard, SUTC and TNO, supported by Transport & Logistiek Nederland, Bouwend Nederland and Topsector Logistiek. Both the construction and logistics sector have their own agreements on information exchange and developed their own standards for different scenarios, but in this specific case it must be investigated whether the existing sector standards also accommodate for exchange of bulk transport information. More specifically: how can the reuse of existing data models accelerate the digitalization of bulk transport information exchange?

During the use case the Wizard functionality of Semantic Treehouse was used to create application profiles reusing existing standards such as the Open Trip Model.

We’re excited to share that our new JSON validator is ready to use! This new validator helps you validate your JSON messages against the agreed schemas in your ecosystem.

With more and more ecosystems relying on JSON to share data, it became essential for us to have a JSON validator in addition to our XML validator. So, we teamed up with a group of students from the University of Groningen, and together we created a first version of this validator.

Here’s what the JSON validator does:​

• Syntax validation: Checks whether th input data is a JSON format.
• Schema validation: Verifies that each element is in the correct structure and validates the rules for required elements.
• Business rules validation: Validation of any extra rules that might be specific to your setup. For example, if a certain element is provided then another element is mandatory.

A special thanks to the group of students from the software engineering course at the University of Groningen who worked with us on this new feature as part of their study project. It was a success because the communities Ketenstandaard and SETU are already using the JSON validator, and we’d encourage you to give it a try as well!

From September 17-19, 2024, we attended the SEMANTICS-2024 conference in Amsterdam, where we delivered a half-day tutorial about semantics in data spaces.

The workshop provided a practical perspective on the role of semantics in data spaces, based on the current challenges we face in practice. We talked about:

• Data spaces and the role of a vocabulary hub,
• How to established semantic interoperability in a data sharing community setting,
• How the bridge the gap between the two paradigms for interoperability: Linked Data based interoperability and (EDI) message based interoperability.

Moreover, we shared future challenges that are currently work in progress for the Semantic Treehouse team:

• How to support the reuse and diversity of existing data models and formats across data spaces?
• How to reduce the amount of manual labour in developing and mapping semantic standards?
• How to integrate semantic web technologies into data space architectures?
• How to improve data quality and the quality of data models?

A key highlight was the hands-on experience for the audience. Within 45 minutes, participants created their own standards using our new demo environment. Want to know more? Please get in touch!

Thanks to SEMANTICS for the opportunity to connect and learn from the community!

Read More​

• Download the presentation slides
• Check our demo environment

In this second IDSA Tech Talk on semantic interoperability in data spaces we shared our view and lessons learned on the role of Application profiles in specifying semantics for data spaces. Thank you IDSA for the opportunity to share and learn from the data spaces community.

View online​

Read more​

• Download the presentation slides
• Last years IDSA Tech Talk on Semantic Interoperability

From September 17-19, 2024, SEMANTICS 2024 will be held in Amsterdam. At this conference, we will have the opportunity to present a tutorial on Semantics in Data Spaces. Our session will focus on Semantic Treehouse as a vocabulary hub. This tutorial offers a practical perspective on semantics in data spaces and presents the open challenges in an interactive manner. It includes an introduction to data spaces and the role of a vocabulary hub in data spaces, based on the current knowledge from the Data Spaces Support Centre.

For more information, to register, and to view the planned agenda for the tutorial, please see below or visit the following link: Register here.

Abstract​

Data sharing accelerates innovations within and across sectors, leading to the development of new solutions. For a growing number of companies, this turns data into a strategic asset over which they want to maintain control. The European strategy for data aims to create common European data spaces to ensure Europe’s global competitiveness and data sovereignty. Data spaces are distributed systems designed to facilitate secure and trustworthy data exchanges between participants, emphasizing trust and data sovereignty. One of the crucial aspect in making this happen is semantics, which enables consistent and unambiguous data sharing between participants of a data space. Achieving semantic interoperability is a continuous balancing act between maintaining strict uniformity for consistent and easily understandable data, and accommodating the diverse requirements of different data space participants.

This tutorial offers practical insights into shaping semantics within data spaces, with TNO’s Semantic Treehouse as so-called vocabulary hub as an important means. The tutorial highlights the challenges and presents solutions for achieving semantic interoperability in the context of data spaces.

Structure and Duration of the Tutorial​

Importance​

Shared data models or semantic standards are relevant means for achieving semantic interoperability in data spaces. These models and standards enable data providers and data consumers to speak the same language when exchanging information. However, just like in the real world, each participant in a data space operates with a different perspective, making the development and governance of these models a standardization process within the data space.

In data spaces, there is technical component that facilitates this standardization process, helping communities agree on, define, and improve these shared semantic standards. According to the IDS RAM 41, such a tool is called a vocabulary hub. This service stores, maintains, and publishes semantic standards while enabling collaborative management of these standards. TNO’s implementation of a vocabulary hub is called Semantic Treehouse. In this tutorial, we show how Semantic Treehouse can be used in creating these models, and how it contributes to achieve semantic interoperability in data spaces.

Audience: expected outcomes and prerequisite knowledge level​

We expect participants to have an understanding of the importance and use of semantic models and standards. Knowledge on data spaces is not required. The outcome of this tutorial is for the audience to gain an understanding of data spaces, particularly how semantic models and standards are developed and used within data spaces, to learn about the tools that facilitate semantic interoperability in data spaces and to discuss the open challenges.

Contact information​

Jelte Bootsma - jelte.bootsma@tno.nl​

Jelte is a consultant in the Data Ecosystems department of TNO. Jelte focuses on data exchange between different technologies and systems. He is involved in several industry standardization initiatives, developing semantic standards for interoperability, especially in the high-tech manufacturing sector. Additionally, his work at the Data Spaces Support Centre involves creating semantic interoperable "data spaces" that allow companies to share data with customers or suppliers while maintaining control over their own data.

Linda Oosterheert - linda.oosterheert@tno.nl​

Linda is a consultant in the Data Science department of TNO. Linda develops semantic standards and advises organizations on how to become interoperable. Her specific area of interest is how organizations can further develop and manage their semantic standards, when there is a lot of dynamics and where a combination of expert knowledge and data is required. Typical clients that Linda serves are industry or standardization organizations. She works in various domains, with a focus on the labor market in recent years with projects such as Vaardig met Vaardigheden, Data Space for Skills and SETU.

Cornelis Bouter - cornelis.bouter@tno.nl​

Cornelis Bouter is scientist on semantic interoperability in the Dutch applied research organisation TNO. His work focuses on applying semantic technology in a range of domains, such as energy, logistics, agriculture, and health. In particular, he is co-author of SAREF and SAREF4ENER. He gained his BSc and MSc on artificial intelligence from Utrecht University.

However, there are various situations that would benefit from a more 'bottom-up' approach, i.e. where the information flow is reversed. Instead of Semantic Treehouse providing information on what vocabulary to use, users provide information on the usage of their data. This need arises in situation where, for example, there's only a loosely defined community where standards are not yet formed. Or when typical users don't have the resources or knowledge to work with semantic standards and make alignments. To overcome this hurdle, Semantic Treehouse has developed a new functionality that allows users to create an initial vocabulary based on their own sample data.

Bottom-up approach​

In its first iteration, users can create a vocabulary by providing a sample of their data. This sample is expected to be in CSV format. The wizard then generates an initial vocabulary based on the data fields in the sample. The user can then start the process of enriching the vocabulary, e.g. by providing definitions for each data field or cardinality constraints. It also serves as a starting point to map these data elements with concepts in common vocabulary standards, if these exist. It can also serve as a practical example to guide the direction of any open standardization process aiming to find a suitable common vocabulary. Since the direction of information flows from users to the centralized standardization effort, we refer to this approach as the bottom-up approach to semantic interoperability.

Example: A very simple CSV file​

Similar to the other inputs, this concept is to upload sample data before specifying your own vocabulary in the message treeview of the wizard. For example, below is a basic CSV file that Semantic Treehouse can import. Semantic Treehouse considers the column names in the CSV file as element names in the generated vocabulary.

Name,Age,Occupation
John,55,Engineer
Alice,32,Doctor
Eve,22,Student

After uploading this CSV file in the Wizard, it will present a message treeview displaying the column names as three potential sub-properties:

• Personal Data
  • [0..1] Name
  • [0..1] Age
  • [0..1] Occupation

What is next?​

Now it is time to edit, maintain or publish this vocabulary for your own domain-specific use case. In the meantime, we are actively working on improving this feature, starting with deriving the cardinality and data types from the CSV file. Additionally, we are exploring ways to improve the reusability of vocabulary by providing a pre-defined definition of the columns based on existing vocabularies.

Sharing data across different sectors holds tremendous value. For example, to achieve EU climate goals, the exchange of carbon footprint data across various sectors is of great importance. Therefore, connecting different data sharing communities in these sectors is essential. However, sharing data across different sectors comes with challenges, as each sector develops its own vocabularies and tools. These vocabularies are not accessible across different sectors, and consequently, thus not being used in other sectors. To brigde this gap, a standardized solution is needed to exchange vocabularies across data sharing communities, enhancing the discoverability of vocabularies across different sectors.

DCAT-AP for Vocabulary Sharing​

The Data Catalogue Vocabulary Application Profile (DCAT-AP) emerges as a crucial standard for data sharing. While primarily devised for public sector dataset and data servcice exchange in Europe, the versatility of DCAT-AP extends beyond datasets to include vocabularies.

Traditionally, vocabularies have been categorized as metadata, providing context for datasets. These datasets and data services can be shared across communities through the open standard DCAT-AP. Our observation indicates that vocabularies can also be considered as datasets, enables us to describe and exchange vocabularies across sectors using DCAT-AP. This empowers data sharing communities to exchange vocabularies as they would datasets, fostering greater adoption and reuse.

Features and Implementation​

Recognizing the potential for DCAT-AP to enhance semantic interoperability, we have integrated DCAT-AP exports functionality in Semantic Treehouse. Through Semantic Treehouse, users can now export the vocabularies of a single project, as well as the entire repository, with the aim of encouraging widespread use and integration into existing systems.

We have incorporated both a user-friendly download button and an API to cater to different needs. The download button offers immediate export, while the API enables direct machine-to-machine communications, ideal for those seeking to automate and integrate vocabulary usage within their systems.

Example​

The example below illustrates an exported project from Semantic Treehouse in DCAT-AP format. It includes metadata about the project (dcat:Catalog), the specification(s) (dcat:Dataset), its versions (dcat:Dataset) and the available distributions (dcat:Distribution). The example is a snippet about the recently published SETU Planning and Scheduling standard.

@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dc:   <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix xsd:  <http://www.w3.org/2001/XMLSchema#> .
@prefix owl:  <http://www.w3.org/2002/07/owl#> .
@prefix sth:  <https://setu.semantic-treehouse.nl/> .

<https://setu.semantic-treehouse.nl/projects/Project_bceb3f48-f0d6-427c-aaf2-faa30b1537b6>
  a dcat:Catalog ;
  dc:description "Collection of specifications for project Planning and scheduling"@en ;
  dc:publisher <https://setu.semantic-treehouse.nl/groups/SETU> ;
  dc:title "Planning and scheduling"@en ;
  dcat:dataset <https://setu.semantic-treehouse.nl/specifications/planning-assignment>,
               <https://setu.semantic-treehouse.nl/specifications/planning-constraints>,
               <https://setu.semantic-treehouse.nl/specifications/planning-request> ;
  foaf:homepage <https://setu.semantic-treehouse.nl/#/Projects> .


<https://setu.semantic-treehouse.nl/specifications/planning-assignment>
  a dcat:Dataset ;
  dc:description "The Planning Assignment message is used to exchange the planning of a worker on a certain position. The message includes information about the position and the exact moments in time the worker is planned. The message is sent from the planning system at either the supplier or customer to the other party."@en ;
  dc:title "Planning Assignment"@en ;
  dc:publisher <https://setu.semantic-treehouse.nl/groups/SETU> ;
  dc:hasVersion <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443> ;
  dc:identifier "https://setu.semantic-treehouse.nl/specifications/planning-assignment"@en ;
  dcat:landingPage <https://setu.semantic-treehouse.nl/#/Message_32_model/MessageModel_2c67bb3d-b1c1-423b-90d5-8cac64eda603> .

<https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443>
  a dcat:Dataset ;
  dc:description "The Planning Assignment message is used to exchange the planning of a worker on a certain position. The message includes information about the position and the exact moments in time the worker is planned. The message is sent from the planning system at either the supplier or customer to the other party."@en ;
  dc:title "Planning Assignment v1.0"@en ;
  dcat:distribution <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-xsd>,
                    <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-json-schema-json>,
                    <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-json-schema-yaml>,
                    <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-oas-json>,
                    <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-oas-yaml>,
                    <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-rdf-shacl-ttl>,
                    <https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-excel> ;
  dc:publisher <https://setu.semantic-treehouse.nl/groups/SETU> ;
  dc:identifier "https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443"@en ;
  dcat:landingPage <https://setu.semantic-treehouse.nl/#/Message_32_model/MessageModel_2c67bb3d-b1c1-423b-90d5-8cac64eda603> ;
  dc:issued "2023-06-20"^^xsd:date ;
  owl:versionInfo "1.0 (RELEASE)" .

<https://setu.semantic-treehouse.nl/specversions/MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443/distributions/message-xsd>
  a dcat:Distribution ;
  dcat:accessURL <https://setu.semantic-treehouse.nl/api/v1/fit/message/Message_107481ae-3662-4cd0-bb35-78095f3b2675/xml/schema?style=doll> ;
  dc:description "Distribution of type XSD for STH specification version with id MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443"@en ;
  dc:format <http://publications.europa.eu/resource/authority/file-type/SCHEMA_XML> ;
  dcat:downloadURL <https://setu.semantic-treehouse.nl/api/v1/fit/message/Message_107481ae-3662-4cd0-bb35-78095f3b2675/xml/schema?style=doll> ;
  dcat:mediaType <https://www.iana.org/assignments/media-types/application/xml> ;
  dc:title "XSD schema distribution for MessageModelVersion_7b82e0ef-9766-49e7-a3e0-7b2c1a596443"@en .

Snippet of DCAT-AP export

Learn More​

Besides a new look and feel, many more improvements have been made in the new Semantic Treehouse front-end:

• Vertical menu bar: by moving the menu from the top to the left, there is more space for displaying the contents of the data models.
• User friendly routing: there is better emphasis on the most important content and fewer steps are required for a user to get to what they are looking for. Also, the search function is available in more views.
• Improved specifications overview: a better specifications overview with limited information on each specification and more focus on the newest specifications. For example, users can directly go to the treeview of a certain specification and the most important and/or recent specifications are highlighted.
• Multi-language support: STH platforms can be displayed in multiple languages. Each platform can set its own preferred language and enable support for other languages.
• Clean, simple user interface: the user interface has been given a modern, fresh and clean appearance, which for example makes comparing specifications in the treeview easier.

Special thanks to Simon from Friendly Pixel for designing and collaboratively developing the new front-end.

Check out the new front-end at any of the Semantic Treehouse platforms. We would like to hear how you experience using the new front-end. We also would like to hear from you if you encounter any bugs or improvements. You can contact us via Discord.

For this Planning and Schedulling standard, there was a desire to repurpose HR Open specification whenever feasible. This involved creating multiple message models by carefully selecting elements from the HR Open Model in JSON Schema and incorporating SETU specific additions and restrictions. Furthermore, SETU's goal was also to create schemas in both JSON and XML formats, along with Open API specifications for seamless implementation and validation. The following text highlights how Semantic Treehouse supported the SETU foundation in reaching their goals.

The SETU foundation has been using the Semantic Treehouse to publish, maintain, and create all of their standards. In addition, SETU uses the Semantic Treehouse for its community portal to maintain content and practical insights from working group meetings. This collaborative space also serves as a hub for providing additional information, such as attendees or the agenda. It allows SETU and their working groups to consolidate everything in a single location.

As mentioned, SETU wanted to reuse the HR Open Specification wherever necessary. For their own SETU-specific concepts and restrictions, they have created their own information model (RDF and SHACL). The Semantic Treehouse provided SETU with the capability to publish these specifications and use them as a starting point within the FIT wizard, enabling them to create their distinct SETU-specific message models.

This empowerment allowed SETU to redesign the structures of HR Open Specifications and change individual elements where needed. This involves introducing new SETU-specific sub-elements, but also customizing the initial elements, for example, by changing the cardinality or the description. In essence, the Semantic Treehouse granted SETU the freedom to shape messages precisely as they envisioned.

Another goal of SETU was to create schemas in both JSON and XML formats, along with Open API specifications for seamless implementation and validation. The Semantic Treehouse assisted SETU by automatically generating schemas for the standards in both XML and JSON formats. Furthermore, the output of the Open API generator within the Semantic Treehouse served as a starting point for SETU to easily create their own API Specification for their standards.

Would you like to know more about the SETU Planning and Schedulling standard, please refer to the SETU documentation page, or feel free to contact us. We are here to help!

Semantic Treehouse offers support to the user communities with their data sharing use cases. We provide a variety of features that allow you to publish, edit, browse, and maintain your own vocabularies. In this blog, I will provide further details about our new JSON integration to support JSON based schema design in Semantic Treehouse.

Before I proceed with further explanation, I would like to first clarify the distinction between the following concepts:

• Vocabularies (artefacts): common language to ensure accurate and consistent data interpretation. This includes ontologies, reference data models, schema specifications, mappings and API specifications that can be used to annotate and describe data sets and data services.

• A vocabulary provider (role): an entity that is responsible for providing(creating, publishing, maintaining) a common language, such as ontologies, reference data models, schemata, etc.

• A vocabulary hub (tool): component providing facilities for publishing, editing, browsing and maintaining vocabularies and related documentation.

Semantic Treehouse currently supports vocabularies expressed in OWL ontologies and SHACL shapes. We are actively working on expanding our support to diverse vocabulary formats from various providers. This includes the development of support for vocabularies written in JSON schema. This means that data models like Smart Data Models, HR Open and the Open Trip Model can also be used. You can publish them or use them as a starting point in the FIT wizard to create your own message model.

Example: A very simple product schema​

Similar to the ontology input, the concept behind the wizard is to select the required JSON Schema before specifying your own message model. For example, below is a basic JSON schema that Semantic Treehouse can import. It considers the cardinality and value constraints specified in the schema.

{
  "$schema": "https://json-schema.org/draft-07/schema",
  "$id": "https://example.com/product-0.schema.json",
  "title": "Product",
  "description": "A product offering and its characteristics.",
  "type": "object",
  "properties": {
    "productId": {
      "description": "The unique identifier for a product",
      "type": "integer"
    },
    "productName": {
      "description": "The name for the product.",
      "type": "string"
    }
  },
  "required": ["productId"]
}

After loading the JSON Schema in the FIT Wizard, it will present a message treeview displaying two potential sub-properties:

• Product
  • [1..1] productID
  • [0..1] productName

Now it is time to edit, maintain or publish this message model for your own domain-specific use case. In the meantime, we are actively working on enhancing this feature, beginning with the resolution of references to other schemas ($ref).

If you would like to know more, please refer to our documentation page of the JSON Schema input, or feel free to contact us. We are here to help!

"Interoperability within a data space requires participants to be able to understand each other. But how do you get data space participants to use a common language? According to the IDS Reference Architecture Model (RAM), the main responsibility for this common language lies with an intermediary role called a vocabulary provider. This role manages and offers vocabularies (ontologies, data models, schemata, taxonomies, codelists) that can be used to semantically annotate and describe datasets and data services. The vocabularies are made available in a vocabulary hub: a service that enables collaborative governance of the vocabularies."

"In the past few months the IDSA Working Group on vocabulary hub worked on elaborating this component and it’s position as data space supporting building block. In this webinar, Wouter van den Berg, one of the contributors to the working group, will tell us more. Wouter is working at TNO as Consultant Semantic Interoperability and Involvement in TNO’s development activities of semantic treehouse; an implementation of such a vocabulary hub."

The video covers:

1. Why semantic interoperability is important
2. The role vocabularies play in IDS
3. The purpose of the IDS Vocabulary Hub
4. A demo of Semantic Treehouse

Go ahead and watch the webinar, and let us know what you think!

Follow this link to download the slides used in the presentation.

You can downloaded the Presentation slides and read our position on the Vocabulary Hub.

About ENDORSE​

The first ENDORSE conference in 2021 marked an important step to surpass these silos and created a unique and much needed type of event on standards, semantics and interoperability. Contributions from researchers featuring emerging data science trends, the United Nations, EU and national public administrations presenting practical use-cases, and the private sector with supporting technical solutions highlighted numerous innovative projects and large-scale activities.

About the presentation​

Data sharing requires participants of a data space to be able to understand each other using a common language or vocabularies. The development of vocabularies is often organized collaboratively by business communities and delegated to a standards development organization (SDO) that publishes and maintains shared domain vocabularies, schemata and API-specifications. In the IDS Reference Architecture Model (RAM), this is called the vocabulary provider, and the platform where communities publish and maintain shared vocabularies is called the vocabulary hub. The hypothesis that we address in this position paper is that a vocabulary hub should go a step further than publishing and managing vocabularies, and include features that improve ease of vocabulary use.

We propose a wizard-like approach for data space connector configuration, where data consumers and data providers are guided through a sequence of steps to generate the API-specifications for their data space connectors, based on the shared vocabularies in the vocabulary hub. Thereby bringing semantic interoperability to data spaces and enabling non-technical domain experts, i.e. people without training or experience in knowledge representation, to collaboratively design the required APIs in a matter of days. Our presentation highlights the importance of semantic interoperability for data sharing and demonstrate an implementation of such Vocabulary Hub.

Read more about the Vocabulary Hub

Terugblik​

Het doel van deze middag was tweeledig. TNO wilde samen met klanten en collega standaardisatie-experts:

1. Kennis en ervaringen delen over het huidige en gewenste gebruik van Semantic Treehouse in verschillende branches
2. Samen de toekomstige ontwikkelroadmap van Semantic Treehouse vormgeven

De middag begon met een opening door TNO waar Michiel Stornebrink tien highlights van het afgelopen jaar aanstipte. Daarna startte een groepsdialoog over trends en ontwikkelingen op het gebied van digitalisering en interoperabiliteit, en welke veranderingen men ziet aankomen in de manier waarop in verschillende branches gegevensuitwisseling is ingericht.

Dit werd opgevolgd door een presentatie van Robert Matoesek (NEN) en een presentatie Herman Drenth (Ketenstandaard) . Beide heren vertelden waar hun organisaties de komende tijd naar toewerken en wat zij nu al doen om die doelen te bereiken.

In het laatste deel van de bijeenkomst ging de groep over op het prioritiseren en aanvullen van de lijst geplande features die TNO had opgesteld.

Het resultaat van de middag is een gezamenlijke Semantic Treehouse ontwikkelroadmap die nog beter is afgestemd op de gebruikersbehoeften. Hier sprongen drie zaken uit:

1. JSON schema validatie support
2. Verbeterde mapping & comparison functionaliteit
3. Verder beschikbaar maken van de Validator API t.b.v. certificatie

Voor al deze drie roadmap items zijn er al concrete plannen om ze te realiseren. Deze bevestiging betekent voor ons een grote motivatieboost om deze functionaliteit zo snel mogelijk te leveren. Waarvoor dank!

Vervolg​

In een volgende blogpost zullen we dieper ingaan op de vernieuwde Semantic Treehouse roadmap. Wilt u de volgende keer ook deelnemen? Dat kan, want volgend jaar doen we het gewoon nog een keer. We gaan nog even kijken of we hier een jaarlijks of tweejaarlijks event van maken. Neem in de tussentijd gerust contact met ons op!

The new website contains several improvements, including:

• User-oriented content to better align with their desire to know how Semantic Treehouse can be used for publishing and maintaining a standard, engaging a user community, and examining implementations technically.
• Our own story, namely the genesis of STH, with a focus on how we have incorporated our own experiences and struggles with developing and managing standards as functionality into the STH suite.
• Customer stories from the various standard development communities that use STH in which they showcase the added value STH offers them.
• Extensive documentation for users, but also developers, community managers and administrators.
• A blog with the latest updates, news items, deep dives in one of the STH components, research topic, our roadmap, talks, etc.

Many thanks to Simon from Friendly Pixel for designing and collaboratively developing the new website.