Making Data More Accessible and Usable: Knowledge Graphs, Semantic Layers, and Vector Databases

The collective fields of knowledge, data, content, and information management have experienced massive changes over the last several years. AI, not long ago a concept mainly reserved for science fiction, has become an everyday tool for information workers. Enterprise search tools, once the definitive answer for information access, findability, and discoverability for enterprises, are quickly falling by the wayside, being replaced, or at least amended, by new semantic solutions that include knowledge graphs, semantic layers, and vector databases. These solutions present new opportunities and new challenges for organizations wishing to unite their many different types of information, making it more accessible, actionable, and usable for their employees, customers, or other stakeholders.

KNOWLEDGE GRAPHS

A knowledge graph is a structured representation of information that depicts entities, their attributes, and the relationships among them in a graph-like format. Unlike traditional relational databases that store data in tables, knowledge graphs leverage ontologies to organize data as a network of interconnected nodes (entities) and edges (relationships).

Each entity in a knowledge graph represents a real-world object, concept, or event. At the same time, the relationships explicitly define how these entities are connected in a machine-readable format, providing context and meaning.

The primary function of a knowledge graph is to integrate disparate data sources and provide a unified, semantically rich view of information without the need to physically move or duplicate content. By linking datapoints based on their underlying meaning, knowledge graphs enable more intelligent data retrieval, analysis, and inference. This semantic understanding allows systems to answer complex queries beyond simple keyword matching, facilitating better decision making and insights. From a user perspective, this means information is being combined in logical ways from different sources, integrated so it can be consumed faster and more intuitively. Rather than searching for, and thus opening, multiple documents or files, a knowledge graph can serve that information up to an end user in an easy-to-consume fashion.

Knowledge graphs power a wide range of applications, from intelligent search engines and recommendation systems to fraud detection and scientific discovery. They enhance data discoverability by providing a clear map of an organization’s information assets and their interdependencies.

This is of particular note, since a single, well-designed and implemented knowledge graph can address many use cases and power multiple applications for an organization.

SEMANTIC LAYERS

The term “semantic layer” is not new, although the concept has drastically shifted in recent years. Traditionally considered within the field of data management only, semantic layers were used to translate technical data into familiar business terms, providing a unified and consistent view of information for analytics and other business purposes. In recent years, the term has matured to be a key component of many AI solutions, going beyond its traditional use for data to be the uniting layer for all of an organization’s knowledge assets, ranging from structured to unstructured, tacit to explicit, internal to external, and more. The semantic layers of today use knowledge graphs, combining them with other design components and technologies, namely taxonomies, ontologies, metadata, and business glossaries, to serve as the middle layer between all of an organization’s repositories of knowledge assets and the applications that can deliver information in an array of use cases.

Each of these components serves a complementary purpose within a modern semantic layer. Taxonomies categorize and establish hierarchies, thereby providing context and controlled vocabularies that facilitate business alignment and shared understanding. Ontologies extend this by offering additional context and alignment, while also introducing connectivity through the definition of relationships between diverse repositories and knowledge assets. The consistent application of metadata across all knowledge assets ensures uniformity and coherence. Finally, business glossaries define the business terminology, which promotes alignment, clarity, and consistency throughout the organization.

Semantic layers connect an organization’s knowledge assets, creating a unified meaning, while assets remain physically separate. This enables easier preparation, querying, and analysis, making all assets relatable. Unlike traditional tools, semantic layers are type-agnostic and add consistent meaning, enhancing findability and discoverability by returning results in context. In addition, semantic layers don’t just return individual results like a traditional search. Instead, they merge existing assets to formulate answers and new material, delivering reliable, organization-specific results. These layers also leverage an understanding of user intent (individual behavior, queries) to infer meaning and anticipate user needs, returning highly accurate, contextualized results. They also build business alignment by providing a shared vocabulary and meaning, serving as a translation layer throughout the organization without forcing changes in communication.

As a flexible middle layer, the semantic layer is not typically user-facing but powers numerous front-end solutions (advanced searches, chatbots, recommendation engines, content assembly, customized content delivery). This drastically reduces per-solution costs and enlarges service scope.

At its core, a semantic layer can supercharge an organization’s AI initiatives, greatly improving relevance, organizational context and understanding, completeness, and accuracy of AI results or answers.