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

VECTOR DATABASES

Although knowledge graphs and semantic layers offer a great deal of value independently, they fall short of a complete solution for the largest and most complex organizations. Vector databases bring machine learning (ML) into the solution set, improving the automation and machine readability of the organization’s knowledge assets.

A vector database is a specialized type of database designed to store, manage, and search high-dimensional vectors, often called embeddings. These vectors are numerical representations of primarily unstructured or semi-structured content (such as text, images, audio, or video) generated by ML models, where similar datapoints are represented by vectors that are numerically close to each other in a multidimensional space. Unlike traditional databases that rely on exact matches or structured queries, vector databases are optimized for similarity searches, which allows them to find items that are semantically similar to a given query vector.

The primary function of a vector database is to enable efficient similarity search and retrieval across large datasets of embeddings. When a query vector is provided, the database quickly identifies and returns vectors closest to it based on various distance metrics (e.g., cosine similarity, Euclidean distance). This capability is crucial for applications that rely on understanding the meaning or context of data rather than just keywords. For instance, in a recommendation system, a vector database could find items similar to what a user has previously engaged with; in a natural language processing application, it could retrieve documents semantically related to a user’s query.

Vector databases are essential for powering a new generation of AI-driven applications, including semantic search, recommendation engines, anomaly detection, facial recognition, and large language model (LLM) applications such as retrieval-augmented generation (RAG). By providing rapid access to contextually relevant information based on vector similarity, these databases significantly enhance the capabilities of AI systems, allowing them to process and understand complex data more effectively and deliver more intelligent and personalized experiences to users.

DECISIONS, DECISIONS, HYBRID DECISIONS

Institutional or domain data provides the specific context in which a given AI model will be applied within the enterprise. Many organizations are seeing better results by employing hybrid approaches to specific use cases and solutions.

For example, one of the top tax and financial services firms is leveraging “semantic routing” techniques to respond with the most accurate and specific information for its search solutions. It evaluates users’ queries and determines the best route to take—general LLMs, basic/vector RAG, or semantic/GraphRAG—to fetch, combine, and deliver a response to user queries.

PUTTING IT ALL TOGETHER

Today, generative AI (GenAI) has made AI more accessible to businesses, specifically by empowering organizations to leverage LLMs for a wide range of applications across data and content. However, the reality is that algorithms trained in one company or on public datasets may not work well on organization and domain-specific problems, especially in domains where industry preferences are relevant. Thus, access to reliable organizational data is a prerequisite for success, not just for GenAI but also for all applications of enterprise AI and data science solutions. This is where knowledge graphs, semantic layers, and vector databases complement each other in modern enterprise AI solutions by tackling different aspects of how organizations understand, retrieve, reason, and reuse vast and diverse data and knowledge assets.

As a result, these solutions form the core engine of enterprise AI through the following:

• Data Ingestion and Understanding: Semantic layers give AI the vocabulary and logic to extract meaning from data and to define, understand, tag entities, disambiguate terms, infer concepts, and annotate content—ultimately bridging the gap between raw data and business understanding.
• Data Structure and Contextualization: Knowledge graphs give AI a machine-readable framework for representing enterprise entities, relationships, and workflows and the memory and context to reason.
• Search, Discovery, and Similarity: Vector databases give AI the scale and agility to discover, through fast retrieval of semantically similar content across all data formats, and drive insights, auto-generate reports, and support decision making.