The Role of Consistent Semantic Data Specifications – Understanding the Knowledge Engineer’s Problem
When Knowledge Engineers create a Semantic Data Specification (SDS), they face a significant challenge: designing a data model that aligns technical and business needs while maintaining clarity, consistency, and adaptability. Why is this so difficult? At its core, a robust SDS must satisfy multiple audiences with different expectations.
Business experts, software engineers, and the systems themselves interact with the SDS from unique perspectives. Each group interprets the data through its specific lens, expecting it to meet its distinct requirements. This often creates a language barrier between domain-specific concepts and technical encoding. Without a shared and unified framework, these perspectives can lead to gaps and inconsistencies in the SDS, which SEMIC aims to resolve by bridging the gap.
1. What is the Core Challenge for Knowledge Engineers?
In knowledge engineering, the real challenge is creating a data standard that everyone can interpret the same way. The task of a semantic engineer is to 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. The knowledge engineer’s challenge, then, is twofold:
🟢 Ensuring Consistency Across Consumer Groups
SDS consumers can be split into three main groups:
→ Business experts – they rely on SDS to make sense of data without needing a technical understanding.
→ Technical experts – they need precise data standards to inform development.
→ The machines – they need syntactically and semantically accurate data models to process information consistently.
The SDS must meet each group’s needs in a language they understand.
🟢 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.
2. The SEMIC Style Guide: Addressing Core Challenges in Knowledge Engineering.
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.
3. Architectural Clarifications in SEMIC
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.
4. Consumer and Editorial Perspectives
A robust SDS model addresses both consumer and editorial contexts, balancing the needs of each group. 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.
5. Conclusion
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.
We invite you to explore this article for more information about the SEMIC Style Guide.
For more information about Semantic Data Specifications, please visit this article.
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.