Knowledge graphs are reshaping how we organize and make sense of information.
By connecting data points and revealing relationships between them, these powerful tools are transforming industries, from search engines to digital marketing.
This article explores the fundamentals of knowledge graphs and how they relate to information retrieval and modern search.
Knowledge graphs: The link between data and meaning
While Google popularized the term “knowledge graph” in 2012, the concept of representing knowledge as interconnected information has roots dating back to the 1980s.
Computer scientists and researchers had long recognized the need for systems that could process information in a way closer to human understanding.
Today, knowledge graphs have emerged as a cornerstone of artificial intelligence and search technology, helping AI systems apply meaning to data by defining relationships between entities.
Beyond improving search results, knowledge graphs offer a powerful way to connect and contextualize information, aligning more naturally with how humans think about relationships.
Yet despite their growing importance, understanding what knowledge graphs are and why they’re necessary remains challenging for many professionals.
Before diving into their practical uses, it’s important to understand the basics:
How do knowledge graphs organize information?
Why were they created?
And what problems do they solve that traditional databases can’t?
Let’s break these concepts down with a simple analogy familiar to digital professionals.
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The note-taking challenge
Imagine you’re managing client accounts at a digital marketing agency. In the early days, with just a few clients, your note-taking is casual and unstructured. Conversations flow naturally:
“Tell me about your marketing efforts.”
“What tools are you currently using?”
“Where do you want to be in a year?”
Your notes capture whatever seems important: Some clients struggle with email marketing, others can’t track social media ROI and some haven’t even started with analytics.
Each document reflects the unique flow of conversation and each client’s needs are loosely captured.
But fast forward two years. Your agency now manages hundreds of clients across diverse industries and your team is overwhelmed with scattered information across numerous documents. As you try to analyze this data, issues arise:
One client lists their tech stack as “HubSpot for everything.”
Another breaks down individual tools like “MailChimp, Google Analytics, Later for Instagram.”
A different client simply notes “basic email and social tools.”
Some mention “Google Ads,” while others say “PPC” or “search advertising” – technically the same thing, but not in their notes.
The information exists in your notes, but now it’s becoming increasingly difficult to work with due to inconsistencies and a lack of standard structure.
This frustration mirrors a common challenge in data management: as data grows, so does the need for structure and consistency.
Let’s explore a potential solution.
The first solution: Introducing ontologies
Your team gathers to address the growing documentation chaos and the solution seems clear: create a standardized database to track client information.
However, it’s quickly apparent that it’s not enough to simply list client data: you need a framework that can capture the diversity and complexity across different client profiles.
In other words, you need an ontology – a structured schema that categorizes and defines relationships among various pieces of information to make sense across clients.
You start by creating a basic ontology:
Client name: A unique identifier for each client.
Industry: To segment by business type.
Current marketing tools: Specific tools in each client’s tech stack.
Marketing goals: Objectives such as “increase conversions” or “improve brand reach.”
Key challenges: Common issues clients face, like “low email engagement” or “limited social media ROI.”
Monthly budget: Information on budget range.
Target audience: Demographic or platform preferences (e.g., “young adults on Instagram”).
With this ontology, you attempt to organize your data, standardizing entries like “HubSpot for everything” into specific components (e.g., HubSpot CRM, HubSpot Marketing Hub).
You consolidate terms like “PPC” and “Search advertising” to “Google Ads.” Dropdowns are added to ensure consistency in future data entry.
The results are promising. You can run simple queries to identify patterns and client needs, such as:
Show all clients using HubSpot as their CRM.
List clients targeting Gen Z audiences.
Identify clients facing challenges with email engagement.
For the first time, you have a structured, searchable overview of your client landscape, giving your team the ability to answer questions without manually sifting through notes. However, as your team continues using this structured data, new challenges arise.
You realize that while the ontology organizes core client information, it falls short when you try to answer more nuanced questions, like:
“Do other clients with low email engagement also use MailChimp or is this an isolated issue?”
“For clients focused on social media ROI, what mix of tools tends to correlate with higher engagement?”
“What budget ranges and tool combinations are common for clients in the ecommerce industry?”
These questions reveal deeper patterns that a fixed ontology struggles to capture.
For example, knowing what tools a client uses isn’t enough to assess whether those tools support their goals or align with their challenges.
The diversity of client needs and marketing scenarios requires flexibility that traditional databases can’t easily provide.
At this stage, it’s becoming clear that while an ontology brings structure, it lacks the adaptability needed to uncover hidden relationships and insights.
Organizing information is just the beginning of a longer journey. As you move forward, you’ll need a system that not only stores facts but also reveals connections – one that can evolve with each client’s unique context and the agency’s growing knowledge base.
The growing complexity
As your team continues using the database, you notice that successful marketing tech stacks aren’t just collections of individual tools. They’re interconnected ecosystems where each component impacts the others.
For instance, clients using Shopify Plus often have different email marketing needs than those using basic Shopify. It’s not just about the platform but also factors like:
The scale of their operations.
Integration requirements.
Automation needs.
Customer data handling.
Team technical expertise.
Your current database can tell you what tools clients use, but it doesn’t capture these nuanced relationships.
As the client list grows from dozens to thousands, it becomes impossible for any one person to make sense of all the data at once.
You try to adapt by adding more fields and tables – tracking compatibility, integration requirements and even common tool combinations.
However, this approach adds complexity without really solving the problem. You start asking increasingly complicated questions:
“Which social media tools tend to yield higher engagement in campaigns focused on platform ROI?”
“What combinations of tools align well within certain budget ranges, especially in ecommerce?”
If a team member has worked on multiple accounts over several years, they might notice patterns, remember which combinations of tools led to success and apply that experience to new clients.
But as your agency scales, this level of personal knowledge becomes nearly impossible to achieve. Imagine having 10,000 clients, each with unique setups and objectives.
No single person can hold all the nuanced information needed to spot patterns and trends across such a vast pool of data.
A knowledge graph changes this by creating a structure that allows for reasoning at a scale far beyond human capacity.
Just as an experienced team member might identify common challenges and successful solutions over time, a knowledge graph can uncover trends across thousands of accounts simultaneously.
It connects tools, challenges, budgets and outcomes in a way that lets your team gain actionable insights from data they couldn’t otherwise analyze.
Transforming data into knowledge: Introducing knowledge graph terminology
As your client base grows, traditional databases struggle to reveal the web of relationships and patterns that drive actionable insights.
A system that only stores isolated data points misses these connections, limiting your ability to see patterns across clients, tools and outcomes.
Here’s where knowledge graphs offer an advantage. They reshape data storage into an interconnected network where relationships emerge and can evolve over time.
In a knowledge graph, information is organized as nodes and edges.
Nodes, also called entities, represent individual data points – such as a client, a tool they use or a marketing goal they have.
For example, imagine three nodes: “Client A,” “HubSpot” and “Increase social media engagement.”
On their own, these nodes are just isolated data points. To make these connections meaningful, we introduce predicates.
Predicates are the terms that define relationships between nodes and create connections within the knowledge graph.
In this example, we might say:
“Client A uses HubSpot” or “HubSpot supports the goal of increasing social media engagement.”
Here, “uses” and “supports” are predicates. They function as labels for the edges in a knowledge graph, transforming individual data points into a network of meaningful relationships.
Predicates add context, letting the graph convey rich, relational insights.
Each node, edge and predicate can also have properties or attributes that add additional context.
For instance, “Client A” might have properties like “budget range” or “industry,” while the predicate “uses” could be timestamped to indicate when they began using HubSpot.
These attributes enhance the meaning of each relationship, making the data even more insightful.
This structured setup enables capabilities beyond traditional databases:
Graph traversal
This allows you to explore relationships across multiple nodes and predicates, mapping out connections in various directions.
For example, with graph traversal, you could trace a path from “Client A” to other clients in the same industry who also use HubSpot and share the goal of increasing social media engagement.
It’s much like exploring connections in a social network, but here, it’s applied to your business data.
Semantic inference
Beyond direct relationships, knowledge graphs can detect indirect patterns.
Suppose you find that clients who use both “MailChimp” and “HubSpot” tend to report higher social media engagement.
Semantic inference helps identify these underlying patterns, giving you insights based on broader trends within the data.
By structuring data into a knowledge graph, you transform isolated facts into a network where patterns and relationships become visible.
Predicates give each relationship a defined meaning, helping reveal insights that wouldn’t be obvious if the data were viewed in isolation.
Rather than seeing “Client A,” “HubSpot” and “Increase Social Media Engagement” as separate entries, the knowledge graph connects these dots, creating an interactive web of knowledge that evolves with your needs.
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Building the foundations of a knowledge graph
Our journey highlights the evolution from scattered, isolated data to a dynamic network where insights emerge from connections.
With a knowledge graph, traditional data limitations fall away, enabling you to see relationships that drive SEO success – whether that’s through smarter keyword clustering, trend spotting or content recommendations based on real user behavior.
A knowledge graph is built on a few key, interdependent pieces:
Nodes (Entities): These represent individual components of knowledge, like clients, tools or marketing goals.
Edges (Relationships): These capture how nodes interact or relate to one another, such as a client “using” a tool or a tool “enhancing” a specific objective.
Properties (Attributes): Both nodes and edges can have attributes, like a tool’s “cost range” or a client’s “industry,” that provide additional context.
These elements may seem abstract, but they are the foundational building blocks of a knowledge graph. They allow for capabilities such as:
Contextual inference: By identifying indirect relationships (e.g., a correlation between a tool and higher engagement), the graph can uncover insights without explicitly programmed rules.
Dynamic recommendations: A knowledge graph can suggest tool combinations or strategies based on patterns from past clients, adapting as new data is added.
Insightful patterns: Through graph traversal, it can analyze multiple pathways to reveal hidden trends, such as which tools succeed in specific client contexts.
A search engine, at its core, applies a similar concept on a much larger scale.
When a user inputs a search query, the search engine uses a massive knowledge graph to infer relationships between keywords, topics and even the context behind the search intent. This knowledge can be used while indexing pages and when returning results.
Just as your marketing team benefits from deeper connections in client data, a search engine leverages relationships to deliver highly relevant results based on interconnected knowledge.
As we’ve seen, this evolution from isolated data to a rich web of connections transforms how we interpret and interact with information.
In the next article, we’ll explore how knowledge graphs help understand complex topics and examine the ones used by search engines.
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