What is a knowledge graph?

A knowledge graph is a structured representation of the relationships between data points that represent real-world objects and concepts. Knowledge graphs are based on a much older concept called the semantic network. These smaller-scale diagrams of interconnected ideas were originally used to map human language, factoring in the meaning behind connections.

Today, the modern knowledge graph is built on top of structured and unstructured data sources. This allows it to use semantic relationships to understand human intent and provide more specific and useful information, rather than simply returning a list of topics that match a keyword or query.

For example, in a software delivery pipeline, rather than generating a standalone alert when a vulnerability is detected, a knowledge graph connects that vulnerability to the affected repository, its dependent services, recent deployments, and responsible teams. This gives teams the full context they need to act quickly and remediate the issue.

A knowledge graph works by connecting individual data points, making it easy to understand how they relate to each other and how they interact. These graphs are living models that integrate datasets from multiple sources, often with different structures or formats, before mapping their intersections and dependencies. While every knowledge graph is unique, they’re made up of the same foundational components.

Entities, relationships, and triples

Entities, relationships, and triples represent the components that make a knowledge graph readable. Think of them like grammar rules, including parts of speech and syntax, which structure a common language between systems.

Entity: The what

In a knowledge graph, an entity is a uniquely identifiable object or concept that represents something in the real world. These can be tangible, such as a person, a YAML file, or a security event, or abstract, such as a threat model or an assigned risk level.

On a knowledge graph, entities are represented by nodes.

Relationships: Connect one entity to another

Relationships refer to the semantic links between entities in a knowledge graph. For example, a relationship could connect an application to a Kubernetes cluster or a vulnerability to a specific library.

On a knowledge graph, relationships are represented by edges (i.e., lines) connecting one node to another.

Triples: Communicate the who, when, where, or why

A triple is a core unit of information that connects entities via their relationships to convey specific information. They’re formed by combining the subject (Entity A), the predicate (relationship), and the object (Entity B).

For example, “Kubernetes Cluster XYZ -> Has Vulnerability -> CVE-1234” tells you exactly which cluster has which vulnerability.

Ontologies and schemas

Ontologies and schemas provide the structure and organizing principles that govern the data flowing into a knowledge graph from various sources. They establish a consistent set of rules that make standardizing and synthesizing datasets possible. Ontologies are often expressed using standards such as the Web Ontology Language (OWL), which defines classes, properties, and constraints that give data shared meaning across systems.

Ontologies: The meaning

An ontology defines the shared vocabulary and conceptual model of a knowledge graph. It specifies the types of entities, the relationships between them, and the rules that give those relationships consistent meaning across systems.

Think of an ontology as the classification system of an airport: it defines aircraft types, runway designations, and communication protocols so that every system and operator interprets information consistently.

Schemas: The structure

A schema defines the structural framework of a knowledge graph. It specifies the allowed entity types, properties, and constraints to ensure data is consistently modeled and queryable across systems.

So if an ontology is the airport classification system, a schema defines what information a flight entry must include (e.g., flight number, destination, departure time) and the expected data types for each field.

Graph models and storage

So far, we’ve covered the physical parts of a knowledge graph (nodes, relationships, and triples), organizing principles (ontology), and structure (schema). To complete the picture, let’s explore how the graph model and storage impact how the knowledge graph functions.

Graph models: The representation layer

A graph model defines how data is structured and represented within a knowledge graph, determining how entities, relationships, and properties are encoded. Where a schema specifies structural constraints and an ontology defines semantic relationships, the graph model governs the data representation layer itself.

The Resource Description Framework (RDF) is a widely used graph model built entirely on triples. As a standardized, machine-readable framework designed for interoperability across systems, RDF uses globally unique identifiers, called Uniform Resource Identifiers (URIs), to ensure that each entity is uniquely and consistently identified across datasets.

Storage: The persistence layer

Where you choose to store your knowledge graph has implications for its performance. Knowledge graphs are typically stored in graph-native or other non-relational databases optimized for modeling and traversing relationships, rather than relying on fixed table schemas. This enables efficient execution of complex, multi-hop queries across interconnected data and supports flexible updates as the graph evolves.

There are two primary options for graph storage:

• Native graph storage is built specifically to host knowledge graphs, allowing a query to jump from node to node, rather than searching through a large index.
• Non-native storage adds a graph “translation layer” on top of a traditional database. While this option may be easier to deploy, it becomes unwieldy and slow when used for larger, complex knowledge graphs.

While some may use knowledge graphs and graph databases interchangeably, they are not the same. Each plays an important role in establishing the architecture needed for a context-rich data processing engine.

While knowledge graphs have been around for decades, the generative artificial intelligence (GenAI) and machine learning (ML) boom has thrust them back into the spotlight. The demand for accurate, explainable, and nuanced insights and recommendations is forcing providers to rethink their data infrastructure.

Knowledge graphs provide structured context that can enhance the training of large language models (LLMs) that power AI- and ML-enabled solutions. With more diverse data inputs and advanced logic, knowledge graphs allow these tools to process complex concepts, interpret them within relevant contexts, and adapt to new information. When used for retrieval augmentation, knowledge graphs can help reduce hallucinations and improve response accuracy.

Knowledge graphs are increasingly used in the DevSecOps space because they provide a “living map” of the entire software delivery lifecycle, including code repositories, test results, support tickets, and much more. By unifying these disparate data streams, knowledge graphs generate context-rich insights for faster, more secure deployments.

Developer productivity and code intelligence

It’s hard to overstate the benefits knowledge graphs offer to software developers, especially when powering AI and ML functionality. Rather than looking at code as a series of text files, a knowledge graph creates an interconnected map of entities, integrating data from other sources, like Jira tickets and Identity and Access (IAM) user permissions. The resulting intelligence helps teams automate manual tasks, improve resource management, reduce errors, and enhance security.

Risk management and compliance

Because knowledge graphs can represent software supply chain relationships, including direct and transitive dependencies, they enable impact analysis across services and infrastructure. By correlating vulnerability data with dependency paths, teams can trace potential attack paths and prioritize remediation based on blast radius and business impact.

Knowledge graphs also support compliance by mapping technical controls to regulatory requirements, improving traceability and simplifying audit evidence collection.

Operations and incident response

By modeling relationships between services, infrastructure, deployments, and dependencies, knowledge graphs support faster root-cause analysis during incidents. Teams can visualize service dependencies, assess blast radius, and identify affected systems more efficiently, reducing downtime and operational risk.

Semantic search and recommendations

Knowledge graphs allow a database to go beyond simple keyword matching to understand the intent behind a query or command. These semantic searches factor in ambiguous meanings, the user’s previous history, potential risks or benefits, and more to identify the information that specifically matches the user's needs.

When combined with AI or recommendation engines, knowledge graphs can also power contextual suggestions to help users discover relevant information without manually navigating documentation or configuration files.

Data integration across systems

Knowledge graphs connect disparate data sources, like AWS logs, Static Application Security Testing (SAST) results, and Real User Monitoring (RUM) logs, to establish a unified, comprehensive view. This context-rich architecture across departments and tools is useful for linking code, issues, pipelines, and services in software delivery.

Any organization with a complex, interconnected data architecture can find value in knowledge graphs via semantic search, risk detection, strategic decision-making, and operational optimization.

Industries that commonly utilize knowledge graphs include:

• Software and DevOps
• Search platforms
• Financial services
• Healthcare and pharmaceuticals
• Manufacturing and industrial

Knowledge graphs offer extensive and varied benefits because they transform large-scale static datasets into strategic assets. At the highest level, this enables businesses to improve efficiency, reduce errors, optimize resources, and innovate faster.

Specifically, knowledge graphs offer benefits such as:

• Breaking down data silos: By integrating and standardizing data from multiple systems, knowledge graphs create a unified, contextual view across the organization.
• Flexible modeling of evolving relationships: Graph-based systems make it easier to add new entity types and relationships without restructuring existing data.
• Contextual analytics and pattern discovery: By modeling how entities relate, knowledge graphs support advanced querying and relationship-based analysis when combined with analytical or AI tools.
• AI augmentation and grounding: Knowledge graphs can enhance AI systems by providing structured, domain-specific context that improves retrieval accuracy and explainability.
• Improved traceability and governance: By explicitly modeling relationships and metadata, knowledge graphs support impact analysis, security visibility, and compliance reporting.

Knowledge graphs have incredible potential, but they are also incredibly complex. They must be carefully planned, deployed, and governed to ensure the greatest return on investment.

Knowledge graphs are sometimes accompanied by challenges such as:

• Data integration and quality: Because knowledge graphs integrate data from multiple sources, inconsistencies, incomplete records, or poor entity resolution can undermine reliability and reduce trust in downstream systems.
• Scalability and performance: Performance depends on the storage architecture and query complexity: native graph databases are designed for efficient multi-hop traversal across highly connected data, while graph layers built on relational systems can slow down as graph size and query depth grow. At enterprise scale, careful indexing and infrastructure optimization are essential.
• Ontology governance: Designing and maintaining an ontology requires cross-functional alignment on definitions, hierarchies, and relationships. Without strong governance and version control, inconsistencies or overly rigid models can limit flexibility and reduce long-term value.

Building a knowledge graph goes beyond building a comprehensive database. Instead, you’re developing an extensive, constantly evolving, and robust ecosystem, which requires significant investment of time and resources. Here are the main steps you’ll want to prepare for.

Inventory sources and define objectives

The first step in building a knowledge graph is to take stock. Outline the workflows, data sources, and technology that will feed into or interact with the graph. Once you have a clear understanding of what currently exists, collaborate with stakeholders, including developers, product managers, security, compliance, and others, to define the project’s objectives.

Draft vocabulary and core ontology

Define the common language or schema that will standardize the data feeding into your knowledge graph. For example, detail all types of entities (e.g., objects, events, concepts) that will be featured in your graph.

Next, develop the ontology, which establishes the conceptual relationships and shared meaning across those entities. The ontology defines how concepts relate, what hierarchies exist, and what logical rules apply across the domain.

Separating structure (schema) from meaning (ontology) ensures the graph remains both consistent and semantically coherent.

Map datasets into the knowledge graph

Map real-world datasets to the defined schema and ontology. This often requires transforming unstructured or inconsistent data, aligning identifiers across systems, and standardizing formats. The goal is to ensure that all integrated data conforms to a shared model, enabling reliable querying and cross-system analysis.

Implement entity resolution and validation

Entity resolution ensures that duplicate or inconsistent representations of the same real-world entity are reconciled. For example, slightly different naming conventions for the same service or repository must be unified. Validation processes should also be implemented to detect inconsistencies, enforce constraints, and maintain data integrity.

Choose RDF or LPG and select the appropriate engine

Your next priority will be choosing between the RDF and the Labeled Property Graph (LPG). This is the “engine” that will determine your knowledge graph’s functionality.

• RDF is a commonly used data model across domains that communicates data via triples (i.e., Subject-Predicate-Object) and is useful for interoperability, sophisticated inference, and unifying data streams.
• LPG is a developer-friendly model that allows you to attach properties to nodes and edges for accelerated and sophisticated querying. This model is best for security use cases, such as root cause or impact analysis.

Establish pipelines for updates, monitoring, and versioning

Knowledge graphs evolve as underlying systems and data change. Establish automated ingestion and validation pipelines, similar in principle to Continuous Integration and Continuous Delivery (CI/CD) workflows, to ensure the graph is consistently updated.

It’s important to implement ontology version control and change management processes so updates can be reviewed, tested, and rolled back if necessary. Additionally, monitoring query performance and data quality should be an ongoing practice.

Other best practices to include

To ensure your knowledge graph is resilient, sustainable, and scalable, follow these best practices.

Use modular graphs and validation pipelines: To make a complex knowledge graph more manageable, consider breaking it into smaller domain-specific graphs, all of which are automatically updated and validated. For example, this allows quality assurance and security teams to own their data while remaining connected to the larger graph.

Test performance and plan fallback strategies: As with any release, it’s important to perform stress tests. Use complex, many-layered queries to gauge your graph’s performance. If performance is slow, it could cause a bottleneck for processes and applications using the knowledge graph. In this case, the system should switch to a standard keyword search to minimize friction.

Plan for ongoing governance and enrichment: Knowledge graphs require continuous governance. As business needs evolve, schemas, ontologies, and identity rules should be reviewed and refined to prevent model drift. Strategic enrichment, such as incorporating new datasets or expanding relationship types, ensures the graph remains accurate, relevant, and valuable over time.

Hybrid reasoning with embeddings

This growing trend combines knowledge graphs with vector embeddings to support hybrid reasoning. While embeddings capture semantic similarity in unstructured data, knowledge graphs encode explicit relationships and domain logic. Together, they enable systems to combine similarity search with structured traversal and rule-based reasoning.

Event-driven and operational knowledge graphs

Modern architectures increasingly integrate knowledge graphs with event streams and real-time systems. By combining historical relationships with live data feeds, organizations can support impact analysis, anomaly detection, and automated workflows. In these environments, the graph serves as contextual infrastructure for operational decision-making.

Multimodal knowledge graphs

Multimodal knowledge graphs integrate structured representations derived from text, images, audio, and other data types. Machine learning systems extract entities and relationships from these sources, which are then modeled within the graph to power unified querying across diverse data formats.

Advancements in graph platforms and hybrid search

Graph-native platforms continue to evolve with improved query optimization, scalability, and integration with vector search systems. For example, hybrid search, combining structured graph traversal with semantic similarity search, is becoming more common in AI-driven applications. Enhanced governance tooling also supports ontology management and version control at scale.