AI that solves real problems in the field is not AI that generates fluent responses. It is AI that understands the structure of domain knowledge and the relationships between concepts.
A knowledge graph is the foundation that makes that understanding possible.
How to Build a Knowledge Graph: A Data Scientist's Workflow
How to build a knowledge graph that makes your AI smarter and more reliable, broken down into 7 steps.
Jun 12, 2026
Contents
What Exactly Is a Knowledge Graph?Components of a Knowledge GraphWhy Do You Need a Knowledge Graph?7 Steps to Building a Knowledge GraphStep 1: Define Scope and Configure the Development EnvironmentStep 2: Define Entities and RelationshipsStep 3: Define PropertiesStep 4: Define Inference RulesStep 5: Define ConstraintsStep 6: Generate InstancesStep 7: Quality AssuranceIs a Knowledge Graph Alone Enough? The Answer Is Neuro-Symbolic.Palantir surpassed a $300B market cap, with U.S. commercial revenue up 137% year-over-year.
Palantir has become one of the most closely watched companies in the AI industry.
AI investment is accelerating across industries, but what makes Palantir stand out? The answer lies in knowledge graphs.
Palantir's CEO Alex Karp has been a vocal critic of what he calls "Slop AI," a term he uses for AI that looks impressive on the surface but delivers little real value in actual business operations.
💡
Palantir's success with knowledge graphs and ontologies as a core competitive differentiator has prompted enterprises across industries to reexamine this technology.
What Exactly Is a Knowledge Graph?
A knowledge graph is a semantic network that structures real-world entities and their relationships in a form that machines can interpret, navigate, and reason over.
A data point in isolation tells you very little.
Take the word "motor." You know roughly what it refers to, but not where in a facility it operates, under what conditions it becomes a hazard, or which processes depend on it.
Without context, you are only capturing about 10% of its true meaning.
A knowledge graph overcomes this limitation. Rather than simply cataloging what exists, it structures how things are connected.
Entities become nodes, and relationships become edges with directionality and semantic meaning.
Together, they form a navigable semantic network that enables AI systems to move beyond simple data retrieval and into context-driven reasoning.
So how does this differ from an "ontology," and why does that distinction matter?
An ontology is the skeleton of a knowledge graph, while a knowledge graph is the fully realized knowledge structure built upon that skeleton.
Ontology: A formal specification that defines what classes of entities exist within a given domain (e.g., motors, processes, sensors), what properties each class holds, how classes relate hierarchically (e.g., an asynchronous motor is a subtype of motor), and what logical rules (axioms) govern valid inference. It is precise, abstract, and serves as the authoritative knowledge schema for its domain.
Knowledge Graph: The building constructed from the ontology's blueprint. It takes the class and relationship structures defined by the ontology and populates them with real instance data. A specific node called Motor_A12 exists, carries a measured temperature of 95°C, is connected to Line 3, and is classified as in an overheated state. In this way, a knowledge graph combines the abstract structure of an ontology with real-world data, giving AI the foundation it needs to perform actual reasoning and decision-making.
The specific role ontologies play throughout the knowledge graph construction process will be covered in detail in the 7-step guide that follows.
Components of a Knowledge Graph
A knowledge graph is made up of several components, but three are absolutely essential. Remove any one of them, and you are left with nothing more than a data list.
Node: An individual entity represented in the knowledge graph. This refers to the actual objects a system recognizes, such as equipment, sensors, processes, and employees.
Edge: The relationship between nodes. This is the critical element that enables AI to move beyond simple data storage and perform relationship-based reasoning.
Property: The detailed attributes attached to nodes and edges. These are the values that concretely describe an entity, such as a motor's temperature, status, and location.
Why Do You Need a Knowledge Graph?
To make that question more concrete:
What does a knowledge graph actually deliver for your organization?
Every technology has its cycle of rise and fall. No matter how promising it sounds, there is no reason to adopt it if it does not deliver tangible value to your organization. Technologies that fail to prove their worth in the field inevitably get overlooked, regardless of the initial excitement.
We expect the current momentum around knowledge graphs to be durable.
🤖
The reason is straightforward. As AI becomes more sophisticated, the need for knowledge graphs grows alongside it. It is becoming increasingly clear that the ceiling on AI performance comes not from the model itself, but from data structure, and knowledge graphs are the technology that addresses that structure directly.
‼️
Progress in AI also means building models that are calibrated to specific operational contexts. Rather than deploying general-purpose AI with no awareness of your domain, organizations can now build systems that understand the relationships within their environment and perform reliably in production.
💻
The advancement of AI also means building AI that is calibrated to real operational environments. Rather than deploying AI that has no context for how your operations actually work, organizations can build AI that understands domain-specific relationships and performs reliably in production environments.
7 Steps to Building a Knowledge Graph
So how do you actually build a knowledge graph? We will walk you through the complete process across seven steps. It is also worth noting that building a knowledge graph requires you to first create a blueprint using an ontology, as the ontology determines how nodes, edges, and properties are defined and connected. Here is what each of those seven steps involves.
At a high level, the seven steps break down as follows:
Steps 1 through 5: Drafting the blueprint using an ontology
Step 6: The moment nodes and edges are generated by populating the ontology with real instance data
Step 7: The process of validating the completed knowledge graph
Step 1: Define Scope and Configure the Development Environment
Scope decisions made at this stage determine whether your knowledge graph succeeds or fails before any data is written.
Failing to clearly define scope at the outset will cause you to lose direction entirely. As related concepts accumulate without clear boundaries, the knowledge graph becomes an overly complex structure that tries to define far more than it should.
That is why the first task at this stage is to define your Competency Questions (CQs): the set of core questions your ontology must be able to answer.
Using manufacturing AI as an example, these questions might look like the following. This list serves as the governing scope definition for the entire ontology.
What processes are affected when Line C goes down?
What inspection procedures should be triggered when a motor overheats?
Who is the responsible party when a specific sensor value exceeds the threshold?
Once scope has been defined, the next step is to configure the environment in which the knowledge graph will be developed. There are two primary tools to consider.
Neo4j: One of the most widely adopted graph databases. Its intuitive node-and-edge architecture makes it ideal for visually exploring and querying knowledge graphs. It uses Cypher, a purpose-built query language, and models data using the Labeled Property Graph (LPG) approach. Recommended if you want to rapidly build and prototype a knowledge graph.
Protégé: An open-source ontology editor purpose-built for knowledge modeling. It supports defining OWL-based classes, properties, and inference rules, and is widely used for designing knowledge graphs that conform to RDF/OWL standards. Recommended if your goal is precision ontology design and building a robust reasoning framework based on open standards.
In short, Neo4j and Protégé serve distinct roles. Neo4j is a property graph database for storing and traversing graph data, while Protégé is a design tool for building RDF/OWL-based ontologies.
Step 2: Define Entities and Relationships
The heart of a knowledge graph is relationships. No matter how well your concepts are organized, if the relationships between them are weak, your AI will not be able to reason effectively. In this step, we will use an ontology to identify the core concepts and structure the relationships between them.
To illustrate Step 2, let us walk through a real-world ontology that has already been built. Take a look at the ontology class association diagram published by the Korean Ministry of Foreign Affairs. The Ministry has made four datasets publicly available: research publications from the Institute of Foreign Affairs and National Security, press releases, briefings, and diplomatic records. As shown in the example below, the process requires enumerating all concepts that influence actual reasoning or decision-making, and then defining the relationships between them.
💡
When these four document types exist in isolation, it becomes difficult to establish connections such as "this briefing and that press release cover the same event" or "this individual is associated with this country." A knowledge graph structures exactly these connections, making them meaningful and actionable.
The Korean Ministry of Foreign Affairs structured region, country, city, person, organization, event, and year as its Core Ontology Classes. The mint-colored circular items in the diagram represent these core ontology classes.
These core classes are linked to the central document types, namely publications, briefings, press releases, and diplomatic records, and published as LOD (Linked Open Data). The structure in which all arrows point toward the center represents exactly these connection relationships.
Red arrows represent SubClassOf relationships, meaning hierarchical relationships. City is a subclass of Region, and Person is a subclass of foaf:Agent.
Gray arrows represent Object Properties, meaning relationships between concepts. Each relationship carries a clearly distinct semantic meaning, as seen in properties such as relatedCountry, hasCity, hasPosition, and relatedDept.
Step 3: Define Properties
The Korean Ministry of Foreign Affairs built this ontology with one ultimate purpose in mind.
"Which diplomatic documents are related to which events, what persons and organizations were involved, and which country does it concern?"
In a knowledge graph, it is properties that bring precision to the connections between nodes. Each individual property can be thought of as a direct answer to that question. Let us examine this through the Ministry of Foreign Affairs' ontology Property structure.
Owl:Thing at the top represents the highest-level concept. Everything that exists within the ontology originates here, with subordinate classes branching downward via rdfs:subClassOf arrows.
Reading from the left, publications, briefings, diplomatic records, and press releases are each defined as classes. Within each class, properties such as postingDate, dataURL, and abstract are defined.
Within the spatial class hierarchy, geo:SpatialThing serves as the top-level concept, from which mofa:Area, schema:Country, and schema:City are derived hierarchically. If only a city is present without a link to its country, upward reasoning to broader contextual levels becomes impossible.
The actor class hierarchy has foaf:Agent at the top, branching into foaf:Person, foaf:Organization, mofa:Division, and mofa:Position.
Looking closely at foaf:Person, properties such as hasPosition, relatedCountry, and relatedEvent are defined with precision.
Defining a person's position matters because two individuals with the same name have entirely different significance depending on whether one spoke as a foreign minister and the other wrote as a researcher.
urthermore, to reason about which country a person was diplomatically engaged with, the person and country must be connected through a property.
One property worth noting is owl:sameAs. It appears across multiple classes and serves to express that two entities existing in different ontologies refer to the same real-world subject.
For example, it explicitly identifies that "Diplomat Kim" in the Ministry of Foreign Affairs ontology and "Kim" in an external institution's database are the same individual. This enables AI to perform reasoning not only within a single ontology but across connected ontologies, extending the scope of inference considerably.
📌
As an additional note, even when your ontology is formally structured, it is good practice to attach natural language descriptions to your classes and properties. There will be moments when plain-language explanations become necessary, such as when collaborating with teams outside the immediate domain.
Step 4: Define Inference Rules
Once classes and relationships have been defined, the next step is to define the inference rules, or axioms, that derive new facts from those relationships.
The goal is to encode the rules your team already follows in practice. Using manufacturing AI as an example, these rules might include the following.
If a motor is in an overheated state and is in use on Process B, then Process B must be halted.
If Process B is halted, then Line C must also be inspected.
If an inspection manual exists and an overheat condition is met, then an alert must be sent to the responsible party.
Without these rules, even the most well-structured knowledge graph leaves AI unable to make independent judgments. It remains a system that stores data rather than one that reasons from it.
One important caution: inference rules are powerful, but overuse becomes a liability. The more rules you add, the greater the risk of conflicts arising or unexpected inference results emerging. It is essential to define only the rules that are genuinely necessary, each with clearly stated conditions.
Step 5: Define Constraints
It is strongly recommended to define constraints in order to prevent invalid data from entering the system and to improve the reliability of inference. Before designing those constraints, however, there is one foundational decision that must be made first.
"What world assumption should our knowledge graph be built upon?"
There are two primary world assumptions to choose from.
Open World Assumption
What is not stated is simply unknown.
This is the approach followed by traditional ontology standard languages such as OWL. To elaborate, the absence of a statement such as "A is B" in the system does not lead to the conclusion that "A is not B." Instead, the possibility remains open as unknown.
This flexibility comes with trade-offs. It handles complex, unstructured knowledge relationships well.
However, it may be ill-suited for certain enterprise systems where a clear distinction between true and false is required.
Closed World Assumption
What is not stated is false.
This is the approach Palantir has adopted. Rather than leaving possibilities open regarding internal enterprise data, it treats the data already present as complete in itself. As a result, the system avoids ambiguous responses and can process true and false determinations with clarity. This structure enables business process automation to execute without interruption.
Palantir also adopts a managed schema based on objects, properties, and links, allowing for stable and reliable operations. It aligns well with conventional software development practices such as object-oriented programming, which shortens the path from design to production deployment.
Step 6: Generate Instances
An instance is what you get when real values are populated into the structure defined by your classes.
Let us use a manufacturing AI knowledge graph as an example.
Knowledge graph structure: Motor - temperature - status - location
Instance: Motor_A12 - temperature = 95 - status = Overheated - location = Line 3
As you begin populating instances, unexpected issues will inevitably surface. You may find that when motor data is entered, an error occurs because the process it needs to connect to has not been defined as a class. An entity such as an auxiliary motor that was overlooked during design may appear, and your existing classes may not be sufficient to distinguish it. You may also encounter edge cases where temperature = 95 was defined as overheated, yet in a specific process, 95 degrees falls within the normal operating range.
Errors that surface during instance population are worth catching here. They indicate structural gaps in the ontology that would cause failures in production.
🧩
No matter how carefully and thoroughly a knowledge graph has been built, errors can still emerge once it meets the realities of actual operations.
Step 7: Quality Assurance
Once errors have been identified and resolved, you have reached the final step. This is where many teams let their guard down. Completing the build does not mean the work is done. Skipping this step creates structural issues that will surface in production and erode adoption over time.
In Step 6, populating instances allows you to uncover structural errors and consistency issues. Step 7, however, requires a separate round of inference testing.
With all data in place, the task is to verify: "Can the AI reason in the way we intended?" At this point, inference rules may have been incorrectly defined, relationships may be broken, or constraints may be inadvertently blocking inference from occurring
Is a Knowledge Graph Alone Enough? The Answer Is Neuro-Symbolic.
That raises a question worth addressing directly: is a knowledge graph alone sufficient?
That raises a question worth addressing directly: is a knowledge graph alone sufficient?
AI researcher Henry Kautz framed the path forward this way:
It is only when the pattern recognition of neural AI (System 1) is combined with the logical reasoning of symbolic AI (System 2) that AI can reliably move beyond its current constraints.
Neural AI detects patterns in the real world. Its strength lies in its ability to capture patterns that were never explicitly defined in the ontology.
Symbolic AI makes actual judgments based on a knowledge graph containing classes, properties, and inference rules. It is the component that applies detected patterns against defined rules and arrives at conclusions.
🔍
AI developed through this process will perform more accurately and reliably than models operating without structured domain knowledge. But accuracy and trustworthiness are not the same thing.
💥
In domains such as manufacturing, healthcare, finance, autonomous vehicles, and defense, where a single misjudgment can trigger a serious incident, AI must be able to explain why it reached a particular conclusion. AI that cannot provide that explanation will struggle to earn trust in operational environments.
Combining neuro-symbolic AI with a knowledge graph addresses this problem directly. It brings your AI to a level where, when a regulatory audit occurs, the reasoning behind every AI decision can be documented and submitted as evidence.
For those who want to develop a deeper understanding of ontologies, knowledge graphs, and neuro-symbolic AI, Pebblous has prepared additional resources.
How Does Palantir's Operational Ontology Differ from a Classic Ontology?
How Can Neuro-Symbolic AI Address GraphRAG's Quality Limitations?
The History of Neuro-Symbolic AI and the Path Forward for Its Application
Pebblous addresses these questions and many more surrounding ontologies and neuro-symbolic AI. If today's article was your starting point, take the next step at the hub below.
If you have worked through this process and the results fell short of your expectations, the issue is likely not the methodology itself. The process of identifying the right structure for your specific organization is often more demanding than it appears.
✅
The content covered in this article is best understood as a starting point rather than a complete solution. In real-world implementations, every enterprise and institution brings a different domain, a different data structure, and a different set of problems to solve. A single article has its limits when it comes to addressing every possible scenario.
Pebblous' clients are moving beyond those limits. That is because Pebblous covers the full scope: knowledge graph design tailored to each organization's domain, validation against real operational data, and neuro-symbolic AI integration.
If you have attempted the build yourself and have not achieved the quality you were looking for, we invite you to reach out to Pebblous. A Pebblous specialist will reach out within two to three business days.
Pebblous brings a structured methodology for building AI-ready knowledge graphs that capture the causal relationships present in real operational environments. Our approach covers world model design that integrates physical laws with domain knowledge, and neuro-symbolic integration that connects AI pattern recognition with logical reasoning, producing AI systems that are both accurate and auditable in production.
Share article