What Is a Semantic Layer?

Making data findable, accessible, interoperable, and reusable (FAIR) across heterogeneous data sources for enterprises is becoming increasingly complex, convoluted, error-prone, and prolonged.

The semantic layer is the missing cog in data management that aims to address the challenges of data literacy, inconsistency, and democratization. By abstracting complex data models in a language that reflects the vocabulary of business teams, it serves as a consistent representation of business data. It provides a unified view across the organization, simplifying access and ensuring better governance.

The semantic layer in a nutshell

A semantic layer is a business-friendly representation of data that elucidates complex business logic in simpler terms. It is a conceptual layer that translates the granular data elements residing in files and data stores to business concepts with a unified view.

In one of the exemplary use cases where a data analyst uses a well-defined business KPI or metrics for analysis, the semantic layer springs into action and maps this KPI or metrics to the underlying data elements. It executes back-end queries across one or multiple data stores and consolidates the results. The semantic layer makes this possible as it is connected to the physical data sources and contains all definitions and business logic.

Traditionally, users need technical expertise to understand database schema and data models to work with the data. The semantic layer abstracts this by bringing a business-oriented perspective of the data, enabling users to interact and analyze it without the technical details.

The need for a semantic layer

Data engineers and data scientists use code-based environments, while data and business analysts rely on low or no-code interfaces. This discrepancy causes ambiguity in data semantics leading to inefficiencies, inconsistency of data definitions, and poor decision-making. The result is wasted time and increased costs due to misinterpretation, miscommunications, mistrust, and more.

The semantic layer bridges this gap by enabling informed decision making across the enterprise for everyone, democratizing data and self-service. This layer contextualizes data relationships, allowing transparency and cohesiveness. It also promotes the reusability of definitions, which makes the data findable, consistent, interoperable, and reusable.

The semantic layer collaborates with other tools in the ecosystem to augment the meaning and context of the underlying data. It elaborates the granular data assets, entities, dimensions, relationships, and conditions and maps the data in a business-consumable form, enabling contextual data discovery.

Current state of affairs

The question that both business and data managers still ask themselves is: “Where to put the business logic”. Ideally, the semantics of business logic should be decoupled from storage, data processing and end-user tools. It should be flexible, agile and democratic so that business users are not dependent on technical teams to adopt changes.

Until now, semantics has been limited in scope and visibility. It was embedded in the Business Intelligence (BI) layer and thus tied into the reporting tools, which made it rigid and vendor-bound. These different tools meant that the definitions of metrics and KPIs differed, leading to additional complexity, confusion and maintenance costs. In current architectures, the semantic information is fragmented across tools, data and metadata silos.

The evolution of the semantic layer with knowledge graphs

Ideally, a semantic layer should provide visibility, accessibility, and interfaces to integrate with data applications and third-party analytics tools. This would enable multiple use cases across domains, technologies, and diverse personas and offer reusability of metrics to build broader context on top of granular data concepts.

This need has prompted the development of a universal semantic layer powered by knowledge graphs. The semantic data model underlying the knowledge graph organizes data in a way that reflects the basic meaning of data items and the relationships among them. This offers ease of reuse, consistency, and shared definitions.

Built on the foundations of the Semantic Web

When we talk about the Semantic Layer, we build on the following basic features of the Semantic Web:

• Uniquely identify resources by assigning a unique address to each data concept for identity resolution, allowing organizations to link and interoperate data.
• Formalize business terminology and rules using the Web Ontology Language, ensuring consistent interpretation of concepts like “customer” or “revenue” across systems. The precise description of the meaning and interdependencies of data elements enables verification and conflict resolution even before the data enters the operational systems.
• RDF triples – allowing facts to be represented as subjects and objects linked by predicates. This enables linking data with meaning, ensuring that concepts are defined and understood at a granular level.
• Business rules – expressed in standard language and linked to ontologies to make sure meaning is shared (not obscured by vague terms or cryptic codes).

It is these fundamental characteristics of the Semantic Web that support the reusability of data – clearly and unambiguously – across systems and processes. The semantically enriched data exists separately from the business logic and code in which it was created, and is available for comprehensive, ubiquitous use by other logics and tools. And lineage and traceability are always guaranteed because each data element is associated with a single, unique identifier and can be tracked as it moves through systems.

From Warehouse, Lake and Fabric to Graph

In the race to become data-driven, most organizations have created a tangled web of data integrations and reconciliations across data silos. It adds up to between 40% – 60% of an enterprise’s technology spend.

Integrating and moving data is not the only problem. Hence, centralizing it in a data
lake or a data warehouse does not resolve the underlying issue because the data
itself is stored in a suboptimal way for extracting insights. Unlocking additional value
from it requires context, relationships, and structure, none of which are present in
how most organizations store data today.

This is where Semantic Technologies came in to turn scattered, dumb data into graphs. Data which alows you to ask questions to and do new types of analytics that were previously impossible. Layering semantics or contextual meaning within the data using ontologies, taxonomies, and controlled vocabularies turns silos, warehouses, lakes and fabrics into what is called a knowledge graph.

Graph enablement can be accomplished at a fraction of the cost of what organi-
zations spend each year supporting the vast industry of data integration workarounds and shadow IT. This approach does not require ripping everything out but rather building a semantic graph layer across data to restore context. It applies equally to data lakes and warehouses – this step increases the value of those investments.

Knowledge graphs can support two major design patterns: semantic knowledge hub and semantic data fabric. The knowledge hub pattern uses knowledge graphs to manage documents and unstructured content, improving relevancy and recommendations. In contrast, the semantic data fabric provides unified access and querying across multiple data sources. Both patterns use semantic metadata, which describes data sources with a conceptual model.

Graph technology excels in the shared representation of unstructured data, which must be integrated in such a way that it can be interwoven with other types of data sources. This is made possible by semantically tagging documents using standards such as the Simple Knowledge Organization System (SKOS) and Schema.org, and linking them to structured knowledge in the semantic layer.

Grounding AI in facts

Reliable facts with precise semantics are specifically important when introducing Generative AI. A knowledge graph provides the necessary grounding to Generative AI to mitigate hallucinations and to feed proprietary data. This makes it an essential component and a reliable information source. With Graph RAG, we use the best of both worlds in hybrid architectures. We leverage the commonly used retrieval augmented generation (RAG) pattern in combination with semantic layers, to retrieve reliable and explainable information for decision-making.

Key benefits of the Semantic Layer at a glance

Its ability to address the complexities of modern enterprise data and AI management improves operational efficiency, data quality, consistency, governance, and agility.

• Data Integration: Combines data from silos into a unified, interconnected view. Breaks down data silos by linking data based on meaning.
• Advanced Reasoning & Inference: Can infer new knowledge based on existing relationships. For example, if “A is part of B” and “B is part of C,” the system can infer that “A is part of C.”
• Contextualized Data: Provides richer context around data, making it more meaningful and useful. The relationships are data, not just metadata.
• Improved Search and Discovery: Enables more intelligent search based on semantic understanding, not just keywords.
• Data Quality and Consistency: Uses ontologies and rules (e.g., SHACL) to enforce data quality and consistency.
• Explainable AI: The graph structure makes it easier to understand why a particular result was obtained (critical for AI applications).

Example: A pharmaceutical company can use a knowledge graph to connect research data, clinical trial results, and patient information to identify potential drug targets or predict adverse reactions. The system can reason about the relationships between genes, proteins, diseases, and treatments. The company starts to benefit from Knowledge as a Service (KaaS).

Three shades of Semantic Layer. What you should know when we talk the semantic layer with knowledge graphs!

Within the realm of knowledge, data, and analytics, the term “semantic layer” has different interpretations. The Business Intelligence (BI) community and the Knowledge Graph community both use the term “semantic layer,” but to describe related yet significantly different concepts.

The BI semantic layer focuses on a consistent, business-friendly data view for BI tools and reporting, acting as a translator between data sources and BI tools. It creates virtualized views, calculated metrics, and hierarchies on top of existing data, relying on relational database concepts and SQL-based access.

In contrast, the Knowledge Graph semantic layer we speak in this website represents data as a network of interconnected entities and relationships, enabling advanced reasoning and knowledge discovery. It uses knowledge graph technology with standards like RDF, OWL, and SPARQL, modeling data as a graph and using ontologies to define data meaning. Recognizing this distinction is crucial for selecting the appropriate technology.