The role of semantic layers and knowledge graphs in enterprise data integration and their importance in creating consistent, unified data representations for efficient governance and decision-making.
Semantic Web standards were originally invented to publish machine-readable data with semantics on the Web. In time, they have proven to be sufficiently flexible and expressive to be used in enterprise contexts for solving data integration challenges by building knowledge graphs.
Nowadays, many enterprises increasingly use knowledge graphs as the core of their data strategy, creating semantic layers and data fabrics to best use their information.
The challenge of data integration
The challenge addressed by semantic layers is the heterogeneity of enterprise data. This includes unstructured, semi-structured, and structured sources that need to be integrated consistently to provide a unified view across all data.
The essential role of the semantics layer is to create a common understanding of the meaning of enterprise data and thereby provide a consistent representation of business facts for humans and applications. This way it drives efficient data governance, supports data quality, and enables inference.
Creating a common representation is especially challenging for unstructured data, which needs to be integrated in a way that interweaves it with other kinds of data. Such a challenge requires semantic tagging of documents using standards, like the Simple Knowledge Organisation System (SKOS) and Schema.org, and linking them to the structured knowledge in the semantic layer.
Semantic data models for standardizing domain knowledge
To execute a semantic layer strategy in an enterprise, it is crucial not only to have the data sources linked into one graph. It is also important to provide explicit semantic schemas to describe the information and align the meaning of the data elements. This will enable humans and applications to have a common ground and source of truth. This way, everyone can understand enterprise data without ambiguities, avoid misinterpretations, and keep it consistent across the enterprises’ data sources.
The key enabler for reusing knowledge is the W3C’s Semantic Web data model, called Resource Description Framework (RDF). It comes with a rich stack of standards for semantic schemes, query languages, protocols, and exchange formats. This allows interoperability, enables decentralized data architectures, and minimizes vendor lock-in. Such standardized domain knowledge is precisely what Generative AI needs as context and grounding.
Graph databases optimize for fast querying and standardized management of interconnected data. Data models like RDF make it easier to share, transfer, and process data. Reasoning using the Web Ontology Language (OWL) and the Shapes Constraint Language (SHACL) can be made most efficient by running it inside the database, as near to the data as possible. Graph databases like Graphwise GraphDB provide enterprise-ready storage and processing for semantic layers, including ACID (atomicity, consistency, isolation, durability) compliance for database transactions, high availability, and enterprise-grade security.
Graph RAG – Generative AI you can trust
Semantic layers easily consolidate enterprise information sources for a unified view on data. On top of that, semantic applications drive business processes and effectively reduce time and costs. Although Generative AI is a powerful new technology applicable to a variety of use cases, the main problem is the completeness and reliability of the information used to train it or to augment generation. Generated false information, commonly known as “hallucinations”, is a show-stopper in an enterprise environment when business decisions require precise and explainable facts.
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.
Building on reliable standards
There are two major graph technology stacks building on top of two different data representation models: RDF and labeled property graphs (LPG). The above story elaborates on how semantic layers and multimodal data fabrics can be developed using the RDF stack.
On the other hand, the LPG model is designed with graph analytics in mind. As good as it is for this purpose, this DNA also makes LPG inappropriate for semantic layers and other applications where data governance, integration, and quality are of the essence. The following capabilities not supported by LPG are a requirement for semantic layers:
- Standardized data schema language and serialization formats
- Formal semantics, reasoning, and validation capabilities
- Publicly available ontologies and datasets
- A flexible data model that can gracefully accommodate the structure and the semantics of diverse types of data, metadata, and knowledge
The semantic layer as the foundation of Enterprise AI
To summarize, semantic layers enable efficient continuous data integration, unification, governance, reuse, and publishing. Graph data can easily be sourced from the semantic layer to LPG for analytics and, if necessary, existing LPG data can also be imported into it.
To serve content management, knowledge management, and Generative AI applications well, on top of GraphDB, Graphwise offers the most comprehensive knowledge graph management platform. This includes PoolParty, which covers the whole linked data life cycle, and a list of accompanying tools and AI models. Graphwise puts knowledge engineers, data engineers, and scientists in control and delivers optimal solutions in terms of both cost and performance.
