Meaningfy

The Meaningfy team attended TED-together 2024, an event organized by the Publications Office of the European Union. Held on December 3-4, this gathering brought together experts, practitioners, and innovators to discuss the latest developments in eProcurement and data sharing across the EU. The discussions focused on the ongoing need for semantic clarity, standardized models, and reusable data structures to ensure efficient cross-border collaboration. Takeaways: 1️⃣ European Single Procurement Document (ESPD-EDM) Updates to the ESPD-EDM and its integration into the eProcurement Ontology support better alignment and interoperability in public procurement systems. These improvements directly address data standardization challenges that organizations face across Member States. 2️⃣ New Features in TED Open Data Service The new capabilities within the TED platform make it easier than ever to access and analyze tender data, giving users the tools to extract meaningful insights. 3️⃣ The need for Improved Data Quality High-quality data is the foundation of interoperable systems that enable better decisions, improved services, and stronger collaboration. Thank you to the Publications Office of the European Union for organizing such an excellent event. Let's keep the conversation going and work together for a more connected, transparent, and interoperable Europe. #TEDtogether2024 #eProcurement #DataInteroperability #

The SEMIC Style Guide was developed by the European Commission to enable seamless communication and interoperability across European Union Member States. Meaningfy was honored to contribute to this effort by developing the Style Guide, which serves as an essential framework for semantic engineers, data architects, and knowledge modelers. This guide provides standardized rules on naming conventions, syntax, and artefact organization to ensure consistency across Core Vocabularies and Application Profiles. Ultimately, the SEMIC Style Guide supports the alignment of domain experts, technical developers, and business users, allowing each to work from the same set of shared, unambiguous concepts. Through these standards, SEMIC helps bring the vision of cross-border, interoperable public services closer to reality by fostering a common language for data across institutions. The SEMIC Style Guide is designed to ensure that data specifications are clear, consistent, and usable by all parties involved: → Domain experts → Software developers → Machines SEMIC architecture focuses on building a unified framework for semantic engineers, data architects, and knowledge modeling specialists to create data specifications accessible and actionable for everyone involved. SEMIC architecture aims to achieve: 🟢 A Common Language for All SEMIC’s standards ensure that business users, developers, and machines “speak the same language”, each understanding the same core concepts. The challenge here is that, without a standard structure, domain experts' knowledge is often lost in translation by the time it reaches developers and their systems. SEMIC’s architectural approach aims to eliminate that gap. 🟢 Seamless Interoperability The ultimate objective of SEMIC’s standards is to design systems capable of seamless interoperability by default. → Business experts define the core concepts that guide system design. → Developers implement these concepts into machine interactions, ensuring systems share data consistently and operate cohesively. → Machines process and share data according to the same standards, fulfilling the business objectives set by domain experts. This process creates a consistent feedback loop where machine interactions align with business expectations. SEMIC ensures that the same concepts apply across human and machine contexts, enabling robust communication and reliable data exchanges. In my next post, I’ll explore how the SEMIC Style Guide addresses the needs of different stakeholders by balancing consumer and editorial perspectives in Semantic Data Specifications (SDS). Stay tuned to learn how SEMIC ensures data is accessible, meaningful, and consistent for everyone involved, from domain experts to developers and beyond. More information about the project here 👇 https://lnkd.in/ejNUtPdY

A robust Semantic Data Specification (SDS) model balances the needs of both consumer and editorial contexts. The SEMIC Style Guide is an essential framework that helps semantic engineers, data architects, and knowledge modelers achieve this balance. Here’s how SEMIC addresses these perspectives: 🟢 Consumer Context (Understanding Each Group’s Needs) Business experts require data to be represented in terms and relationships that are meaningful and relevant to their domain, enabling them to apply it effectively in real-world scenarios. On the other hand, developers need a technical framework that aligns with these business expectations and can be implemented seamlessly in functional systems. Finally, the systems themselves require a precise, machine-readable format that adheres to the same conceptual standards defined by the business experts. The SDS accomplishes this by providing visual, textual, and machine-interpretable representations. Visual and textual representations cater to human users (business experts and developers), while machine-readable representations ensure that software systems can process and interpret the data correctly. This multi-representational approach is essential for any SDS to stay relevant and accessible to everyone involved. 🟢 Editorial Context (Keeping Everything Aligned) Creating a Semantic Data Specification (SDS) is far from a one-time effort. As the model evolves, maintaining consistency across artefacts becomes essential but can easily grow into a significant editorial burden. The editorial process is where much of the intensive work of SDS creation takes place, as editors ensure that each representation (from UML diagrams to SHACL data shapes and OWL ontologies) aligns efficiently with a single conceptual model. Without precise editorial synchronization, updates in one artefact can introduce inconsistencies throughout the SDS, undermining stakeholders’ confidence in the specification’s coherence and reliability. The SEMIC Style Guide provides a reliable framework to address these challenges, equipping Knowledge Engineers, domain experts, and developers with the tools to build coherent, interoperable systems that bridge the gap between human and machine contexts by creating a shared language that works for everyone and ensures the data we rely on is always meaningful and reliable. More information about the SEMIC Style Guide: https://lnkd.in/emkmcFGE More information about Semantic Data Specifications: https://lnkd.in/e2tDfAch -- The SEMIC Style Guide was developed by the European Commission to enable seamless communication and interoperability across European Union Member States. Meaningfy was honored to contribute to this effort by developing the Style Guide, which serves as an essential framework for semantic engineers, data architects, and knowledge modelers.

Data Summit Luxembourg 2024 is approaching, and we’re proud to contribute as a Silver Sponsor this year. On 11 December 2024, at the European Convention Center Luxembourg, the Meaningfy team will connect with industry leaders and explore how data drives meaningful change across the public sector. We invite you to visit our booth and discover our data solutions, which enable a more efficient, transparent, and interconnected European public sector. Whether you’re curious about the latest advancements or want to discuss real-world challenges, we look forward to connecting with you. What to expect from the event: 🟢 Discover the latest advancements in data sharing, analysis, and visualization. 🟢 Gain insights from impactful projects and real-world applications. 🟢 Network with experts across industries and sectors. Event Details: → Date: 11 December 2024 → Time: 9:00–18:00 → Location: European Convention Center Luxembourg (ECCL) Registration link - https://datasummit.lu See you at the summit! #DSL2024 #DataMatters #LNDS #DataJourney

Thank you to our colleague Adrian S. for representing Meaningfy and delivering a powerful keynote at the Golden Juggernaut Innotech Forum 2024 in Moldova! #InnoTech2024

The core challenge for knowledge engineers is creating a data standard that everyone can interpret in the same way. A semantic engineer must ensure that data specifications align with business needs, software requirements, and software systems' needs. The challenge is both technical and editorial. Engineers must bridge semantic needs with technical implementation to make SDS applicable across systems while maintaining conceptual integrity: 🟢 Ensuring consistency across three main consumer groups → Business experts rely on SDS to make sense of data without needing a technical understanding. → Technical experts need precise data standards to inform development. → The machines need syntactically and semantically accurate data models to process information consistently. 🟢 Editorial coherence and maintenance Developing an SDS requires the creation of different representations for different audiences: → Human-readable documentation → Machine-readable formats → User interfaces Without an integrated approach, the risk of misalignment between these representations grows as systems evolve and specifications change. To address these issues, knowledge engineers need an SDS architecture that includes clear role definitions, separation of concerns, and a reliable method for maintaining consistency, also known as the “Single Source of Truth” approach. The European Commission developed the SEMIC Style Guide to address these challenges and enable seamless communication and interoperability across European Union Member States. Meaningfy was honored to contribute to this effort by developing the Style Guide, which serves as an essential framework for semantic engineers, data architects, and knowledge modelers. This guide provides standardized rules on naming conventions, syntax, and artefact organization to ensure consistency across Core Vocabularies and Application Profiles. Ultimately, the SEMIC Style Guide supports the alignment of domain experts, technical developers, and business users, allowing each to work from the same set of shared, unambiguous concepts. Through these standards, SEMIC helps bring the vision of cross-border, interoperable public services closer to reality by fostering a common language for data across institutions. In my following posts, I will present how the SEMIC Style Guide addresses core challenges in knowledge engineering. More information about the project here 👇 https://lnkd.in/emkmcFGE 

As interoperability becomes essential across industries, the demand for structured frameworks like Semantic Data Specifications (SDS) is only going to grow. In the coming years, organizations that prioritize well-defined SDS will likely find themselves far better equipped to manage complex data environments and adapt to new standards efficiently. SDS offer practical, structured solutions to data consistency, clarity, and interoperability challenges. By unifying artefacts like ontologies, data shapes, and technology-specific schemas, SDS frameworks preserve data meaning across various platforms and systems, ensuring that data models remain accurate and adaptable. SDS creates a foundation for data that can be truly understood and applied in different contexts without losing integrity. It provides a common language that reduces misinterpretations and promotes collaboration across diverse teams and platforms. For instance, by establishing Core Vocabularies and Application Profiles, SDS can adapt to specific local requirements while remaining interoperable globally, a balance vital for large-scale projects like DCAT. SDS frameworks enable organizations to build data models that meet specific requirements and align with broader, standardized structures. SDS ensures that data is accurately represented, shared, and understood through broad Core Vocabulary, adaptable Application Profiles, or detailed Implementation Models. In data-driven environments, Semantic Data Specifications ensure that data flows freely across boundaries, making it a shared language understood by systems and people.   For more information about Semantic Data Specifications I invite you to explore this blog article: https://lnkd.in/e2tDfAch __ 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 together, we can find the best solution.

Implementing Semantic Data Specifications can be more complex than it first appears. The process involves carefully maintaining consistency across different components, accommodating various technical needs, and finding a workable balance between flexibility and the specificity required for particular applications. Here are some common obstacles and practical solutions: 🟢 Maintaining Consistency Across Artefacts One of the biggest challenges in SDS implementation is keeping all artefacts aligned. With multiple components, from ontologies to data shapes and technical schemas, inconsistencies can arise if any artefact is updated independently. This makes version control and alignment critical. When one artefact is updated, all related artifacts should reflect those changes to maintain coherence. 🟢 Technology Constraints and Adaptability Different teams may require different data formats (JSON for APIs, XML for document-based applications, or SQL for databases). An effective SDS should bridge these needs without creating misalignment. Including technology-specific artefacts in SDS ensures that each team has what they need, but managing these formats is key to keeping the SDS usable across diverse platforms. 🟢 Balancing Broad Use with Specific Constraints While Core Vocabulary is designed for broad applications, Application Profiles, and Implementation Models add constraints to fit particular use cases. Keeping these definitions and constraints separate is essential for flexibility. A Core Vocabulary should define terms broadly, while Application Profiles specify usage without altering the original definitions. This separation prevents conflicts and enables SDS to scale without becoming too rigid or context-dependent. Implementing Semantic Data Specifications is a balancing act, but overcoming these challenges ensures data remains reliable, adaptable, and ready to support informed decisions across any platform.

A notable example of Semantic Data Specifications (SDS) in practice is DCAT (Data Catalog Vocabulary). Developed to standardize data catalogs, DCAT brings together the artefacts we’ve discussed (ontologies, data shapes, and technical formats) to create a unified vocabulary that makes data catalogs interoperable across various organizations and countries. 🟢 What is DCAT? DCAT is a Core Vocabulary that defines the basic structure for cataloging data. It provides terms like “data catalog,” “dataset,” and “distribution,” each with specific properties and relationships. This basic vocabulary enables different organizations to use consistent terms and definitions when setting up data catalogs, making it easier for everyone to understand and share data. 🟢 How Application Profiles Extend DCAT Application Profiles allow organizations to adapt the core vocabulary to meet specific needs. For example, Romania might create a DCAT Application Profile (DCAT-AP) that adds constraints relevant to their national data cataloging standards. Using Application Profiles, organizations can customize their SDS to fit local regulations or policies without losing the general structure of DCAT, ensuring that their data remains compatible with the broader data ecosystem. 🟢 How DCAT Serves as a Core Vocabulary DCAT is a Core Vocabulary designed for cataloging data in a flexible and reusable way. By providing a base vocabulary for terms like “dataset,” “distribution,” and “data catalog,” DCAT enables diverse organizations to understand and implement these terms consistently. As a Core Vocabulary, DCAT focuses on essential definitions, making it adaptable for different contexts without sacrificing clarity. DCAT exemplifies the power of Semantic Data Specifications, providing a versatile vocabulary that adapts locally while aligning with global standards. To learn more about real-world applications of Semantic Data Specifications, connect with the Meaningfy team for personalized guidance tailored to your needs. For additional insights, we also invite you to explore our next article: https://lnkd.in/eJUQ-2Zb __ 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 together, we can find the best solution.

In the context of Semantic Data Specifications (SDS), an artefact is any component or materialisation that defines, structures, or conveys a data model in a specific representation. Artefacts can be machine-readable (enabling software systems to interpret data accurately) or human-readable (helping knowledge engineers, analysts, and stakeholders understand the model). Artefacts make data models actionable and interoperable across different systems and contexts. Each artefact addresses specific requirements, concerns, or use cases, contributing to a cohesive, organized framework for consistent data sharing. Artefacts are categorized based on their function and representation format, typically including: 1. Ontologies (Core Vocabularies) An Ontology, also referred to as a Core Vocabulary, is a formal, machine-readable definition of the concepts and relationships in a data model. Core Vocabularies aim to be reusable and context-neutral, capturing the fundamental characteristics of entities like “person” or “data catalog.” By defining terms, classes, and relationships in a broadly applicable way, ontologies provide the foundation for shared understanding and data interoperability across contexts. 2. Data Shapes (Application Profiles) While ontologies define terms, data shapes tell us how to use them in specific cases. Application Profiles tailor an ontology to fit a particular context by adding constraints. For instance, an Application Profile might specify that a "data catalog" must have at least one "primary topic." By setting rules for usage, data shapes ensure the data model can adapt to different applications without losing consistency. 3. Technical Artefacts (Schemas in JSON, XML, or SQL) SDS includes various technical artefacts such as JSON-LD, XML schemas, and SQL scripts to make data specifications practical. These artefacts translate the data model into specific formats that allow it to move between systems. Each format supports the same underlying model but caters to the requirements of particular technologies, ensuring that data maintains its meaning regardless of the technical environment. Common Artefacts Used in Semantic Data Specifications: 🟢 Persistent URIs These unique identifiers serve as stable references for terms, supporting consistent data interpretation across systems and over time. 🟢 OWL and SHACL Representations OWL (Web Ontology Language) formally defines the relationships between terms in an ontology, while SHACL (Shapes Constraint Language) sets rules on how terms can be used. OWL supports the core vocabulary, while SHACL provides the structure for Application Profiles. 🟢 UML Diagrams Unified Modeling Language (UML) diagrams visually represent the data structure, showing classes, attributes, and relationships. They’re especially useful for non-technical stakeholders to understand data models. Continued in the comments 👇