Semantic Data Fabric: why giving your data a semantic layer through ontologies matters

The knowledge graph is the heart of Semantic Data Fabric

The Semantic Data Fabric solution is based on the construction of a unified knowledge graph that acts as the mind of the system, serving other components, and is represented semantically — that is, modelled through an ontology.

An ontology represents concepts belonging to a specific part of the world; it can therefore be understood as managing highly specialised knowledge independently of the mode, language, location or writing system in which it is expressed. The ontology or ontological model operates as a system for representing and interpreting the body of content and digital resources within a given knowledge domain, as well as any objects potentially linked to them — and, crucially, it understands the way in which this entire set of entities is interconnected.

This approach goes beyond the data lake, which makes datasets available to users. It represents a comprehensive, advanced vision of data management and exploitation, enabling knowledge to be discovered in a deeper and more intuitive way.

In this sense, the semantic layer of knowledge graphs allows all the information stored in an organisation's various databases to be queried and semantically interpreted. It also enables natural language processing and understanding — essential for extracting entities from the digital resources held across the multiple repositories that organisations of any significant size typically maintain.

This allows people to query information simultaneously and efficiently. In short, this approach facilitates the integration of knowledge management, informal learning and collaborative working within a Linked Data environment.

The semantics and formal representation underpinning the use of ontologies make it possible to represent data and metadata of different types within a knowledge graph in such a way that they can be analysed and interpreted together, without ambiguity or loss of information. This provides a solid foundation for effective data integration and unification.

Requirements for a Data Fabric platform

To understand how Semantic Data Fabric meets the demands of modern business environments, it is worth examining the conditions a system governed in accordance with Data Fabric requirements must satisfy, and how these are met through a semantically represented knowledge graph.

A Data Fabric platform requires a data structure and management architecture that can optimise access to distributed data and organise it intelligently. The knowledge graph consolidates data into a digital ecosystem built on this unified structure.

It must offer self-service delivery to data consumers, being queryable by both people and machines in an unmanaged way. The knowledge graph meets precisely this condition, enabling direct queries without constant technical intermediation.

The platform must increase the value of the organisation's data by giving users access to the right data at the right time, regardless of where it is stored. The knowledge graph unifies heterogeneous, distributed data, eliminating barriers of location and format.

It requires a data architecture that is agnostic with respect to environments, processes, data use and data location, while integrating core data management capabilities. The knowledge graph has an ontology-based knowledge representation model that provides this independence.

Finally, it must automate data discovery, governance and consumption, delivering business-ready data for analytics and AI. The knowledge graph functions in practice as a cognitive artefact that enables the cognitive processes of reasoning, knowledge discovery and inference.

Requirements for a Data Fabric platform	Knowledge graph
A data structure and management architecture that can optimise access to distributed data and organise it intelligently.	Consolidates data into a digital ecosystem built on a knowledge graph.
Self-service delivery to data consumers.	Queryable by both people and machines in an unmanaged way.
Increasing the value of the organisation's data by giving users access to the right data at the right time, regardless of where it is stored.	Unifies heterogeneous, distributed data.
A data architecture agnostic with respect to environments, processes, data use and data location, while integrating core data management capabilities.	Has an ontology-based knowledge representation model.
Automation of data discovery, governance and consumption, delivering business-ready data for analytics and AI.	Functions in practice as a cognitive artefact that enables the cognitive processes of reasoning, knowledge discovery and inference.

Benefits of Semantic Data Fabric

Semantic Data Fabric takes a unifying approach to integrating data sources by using metadata to create a virtualised data layer, avoiding the need to move data from its storage location and preserving its governance. It also adds semantic knowledge (business and industry ontologies) for context and meaning, along with data enrichment processes.

One of the key advantages of knowledge graphs is their ability to create a semantic layer within an organisation's data ecosystem. By integrating datasets from diverse sources with different structural characteristics, knowledge graphs provide a framework that facilitates understanding of the underlying meaning in data. These elements help systems distinguish words with multiple meanings — such as Apple the brand and apple the fruit.

For a system to "interpret meaning", it must be written in a technical language — such as an ontology expressed in OWL — that enables machines or systems to "understand" and correctly handle the set of represented entities, thereby collaborating with people in their querying, information retrieval and knowledge discovery processes. This capability materialises in six strategic benefits.

1. Breaking down silos

Knowledge graphs enable information silos to be broken down. Using knowledge graphs does not mean imposing another format on data; rather, it overlays a semantic data fabric that virtualises or materialises data at a level of abstraction closer to the way users want to work with it. Multiple, varied "views" of the data are now possible without modifying the data at source or in the host system.

2. Normalisation

The ontological model and data management tools enable master data to be managed through the definition of specific entities or by modelling thesauri, taxonomies or normalised classification systems, where the nature of the data is well suited to these structures.

3. Provenance and security

Data is loaded into the knowledge graph along with provenance information, which includes the security terms under which the data owner makes it available (data contract security). This security travels with the data throughout every process in which it is involved — both in its transformation and its publication — limiting its uses and visualisations. This contributes to the overarching control axes: confidentiality, integrity, authenticity, traceability and availability.

4. Enrichment and evolution

The use of a knowledge graph enables information to be enriched and to evolve — in other words, to learn — through two complementary routes.

First, a graph has the ability to link new datasets, thereby enriching the knowledge held within the original datasets. The evolution of the model and the growth of its expressiveness is independent of the construction of the original model, thanks to the adoption of semantic standards and linked data best practices in its design and construction.

Second, through the application of Deep Learning technologies in Natural Language Processing (NLP), which serve to automatically and supervisedly tag (for example, categorise) the system's resources with metadata.

5. Open Data and FAIR Data

The Semantic Data Fabric solution includes a publishing interface for semanticised data from the unified graph into Linked Open Data spaces, which is the optimal way to publish FAIR Data.

6. Standards and governance

Semantic Data Fabric supports compliance with data quality and governance standards, while enabling smooth data exchange between applications and AI models.

Data lake or data swamp

Data lakes, although promising in theory, often become "data swamps" — vast accumulations of information with no meaning or structure. This approach of mass, indiscriminate storage creates problems of visibility, usability and data quality, making it difficult to extract real value.

To overcome these limitations, it is necessary to evolve towards a semantic data lake consolidated in a unified knowledge graph — that is, a Semantic Data Fabric solution. This approach gives meaning and context to stored data, facilitating its interpretation, analysis and effective use in decision-making.

The formula is straightforward: Data Lake + Semantic Layer = Semantic Data Fabric

Data exchange between applications and AI models

A semantic business data layer goes beyond simple business data aggregation. It materialises an organisation's data with specific business context, enabling LLMs and Generative AI tools to understand the data they work with and generate accurate outputs.

Decision-making must be grounded in reliable data. Organisations should not implement generative AI solutions without first considering the quality and actual meaning of their data. This caution stems from three fundamental issues.

1. LLMs, while capable of generating large volumes of human-quality text quickly, may be unable to process technical data correctly, leading to inaccurate responses (also known as "hallucinations") and flawed decision-making. The responses provided by AI can sometimes be inaccurate, and there is often little or no way for human users to determine where they come from.
2. Many applications and cloud services that currently deploy Gen AI-powered chatbots only function as standalone applications or within specific cloud environments, limiting their ability to deliver business intelligence across the organisation.
3. Furthermore, effective generative AI tools must enforce data governance protocols to ensure that employees can only access information relevant to their roles. They must be protected against unauthorised access and must guarantee that users can draw on the full knowledge of their organisation through a single platform. The security of organisational data must therefore be considered — data that, in open generative AI tools, will be used to make decisions and may subsequently be available to competitors.

Because of these issues, organisations looking to use generative AI need to rely on forms of data architecture that can give users of this technology the skills and time needed to implement their solutions internally. Generative AI tools must incorporate business context and ontologies to help them operate safely and effectively.

This is where the application of a centralised semantic business layer helps. Semantic ontologies — which can be private to the organisation — and knowledge graphs "ground" LLM outputs in reality, defining what is and is not contained within an organisation's digital environment, and preventing errors, hallucinations and inaccuracies.

The data structure guides the deployment of generative AI, as the data within the structure is secure, high quality and commercially meaningful, making it straightforward to connect any generative AI tool — including one designed specifically for the organisation.

Relationship between Semantic Data Fabric and UX ergonomics

The ontological modelling of a Semantic Data Fabric is not limited to solving problems related to knowledge representation, data modelling and the way data can be interpreted and used by machines and systems. It goes further, establishing a direct relationship between these models and the needs and interests of people.

In other words, it makes it possible to address questions that fall within the discipline known as Human-Computer Interaction (HCI), which brings together the core knowledge that, from a product perspective, shapes what is referred to as User Experience (UX).