What Do Most People Get Wrong About Knowledge Graphs?

Having become almost synonymous with Neo4j, the company that first brought them into the spotlight, knowledge graphs are becoming a staple technology in modern AI apps. But do we truly understand what makes them so powerful?

In this post, we'll clear up some of the biggest misconceptions around this innovative method of semantic data structuring and show you how to effectively harness their potential—whether you're just starting your graph journey or seeking to enhance an existing implementation.

Misconception #1: Knowledge Graphs and Graph Databases Are the Same Thing

Likely the most common belief about knowledge graphs is that they are simply another type of database.

While graph databases like Kuzu and Neo4j excel at storing and querying connected data, they don't inherently focus on contextual organization of the information they’re fed. In contrast, by extracting entities from ingested information and establishing semantic relationships between them, knowledge graphs create a data representation that emulates human understanding. When building a knowledge graph, we care more about meaning than we do about the engine speed or other database performance metrics.

To illustrate the point, in a graph database, you might store that "Alice bought a book" as one node, and cause yourself a headache down the line. In a knowledge graph, you'd represent Alice as a Person node, the book as an Object node, and create a Purchase event that connects them—along with additional contextual information like when and where the purchase occurred. This rich semantic structure enables more nuanced reasoning and inference.

# In cognee, this might look like:
from cognee import DataPoint
from typing import List

class Person(DataPoint):
    name: str
    metadata: dict = {"index_fields": ["name"], "type": "Person"}

class Book(DataPoint):
    title: str
    metadata: dict = {"index_fields": ["title"], "type": "Object"}

class PurchaseEvent(DataPoint):
    buyer: Person
    item: Book
    date: str
    location: str
    metadata: dict = {"index_fields": ["date", "location"], "type": "Event"}

This structure allows us to ask questions like "What did Alice buy last month?" or "Who purchased books at this bookstore?" in a way that mirrors how we think, rather than requiring complex SQL joins or predefined query patterns.

Misconception #2: More Data Always Means Better Knowledge Graphs

There's a widespread idea that the value of a knowledge graph scales directly with its size—the more data you add, the more powerful it becomes. While there's some truth to this, it misses a crucial point: quality and relevance matter more than sheer volume.

A well-designed knowledge graph with carefully curated entities and relationships will outperform a massive but poorly structured one every time. Adding irrelevant or low-quality data can actually degrade performance by introducing noise and creating false connections, thus obscuring meaningful insights.

The key is to focus on:

• Ontological clarity: Defining clear entity types and relationship categories
• Data quality: Ensuring accuracy and consistency in your source data
• Relevance: Including only information that directly supports your specific use cases
• Proper integration: Connecting new data points to existing knowledge in meaningful ways.

At cognee, we've found that a targeted approach to building knowledge graphs—focusing on high-quality data points with clear semantic relationships—yields far better results than simply ingesting as much data as possible.

Misconception #3: Knowledge Graphs Need Massive Datasets

When people think of knowledge graphs, they often picture Google's Knowledge Graph or similar large-scale implementations. This creates the impression that knowledge graphs are only valuable or feasible for organizations with enormous datasets and resources.

In reality, even smaller, strategically constructed knowledge graphs can be powerful tools, as long as the data can be successfully converted into the knowledge graph structure. The main value-add is connecting the internal company data to the LLM in such a way that the model can efficiently access it.

For example, a startup might build a knowledge graph around their product catalog, customer interactions, and market research. This relatively small graph can still power personalized recommendations, improve customer service, and uncover market insights that would be difficult to extract from conventional data structures.

As we saw in the first example, we don't necessarily even need a database to build a knowledge graph. At its core, a knowledge graph is a structured way of representing and connecting information—mirroring how we naturally organize knowledge about the world (like the human mental lexicon). While databases, as information sources, can support this, they're not essential; knowledge graphs can exist independently of them.

Misconception #4: Knowledge Graphs Are Too Complex to Implement

Many developers shy away from knowledge graphs because they seem complex and challenging to implement. While it's true that building a knowledge graph from scratch requires careful planning and lots of effort, modern tools and frameworks have significantly lowered the barrier to entry.

With libraries like cognee, you can define entity types and relationships using familiar programming patterns, with the underlying graph structure being generated automatically. This abstraction layer makes it possible to work with knowledge graphs without deep expertise in graph theory or specialized query languages.

# Creating and connecting nodes in cognee is straightforward:
alice = Person(name="Alice")
book = Book(title="The Graph Revolution")
purchase = PurchaseEvent(
    buyer=alice,
    item=book,
    date="2024-03-09",
    location="Online Bookstore"
)

# Adding to the knowledge graph is a simple function call
await add_data_points([alice, book, purchase])

The complexity comes not from the technical implementation but from the conceptual modeling—deciding which entities and relationships to represent. This is a challenge with any data modeling approach, not just knowledge graphs. To better understand how to do this yourself, try out Neo4j Graph builder or Graphiti interface.

Misconception #5: Knowledge Graphs and Vector Databases Are Competing Technologies

With the rising popularity of vector databases and vector search for AI applications, a tendency has emerged to view knowledge graphs and vector embeddings as competing approaches.

In reality, these technologies are complementary, each addressing different aspects of knowledge representation:

• Vector databases excel at capturing semantic similarity and handling unstructured data like text, images, and audio.
• Knowledge graphs excel at representing structured relationships and enabling logical reasoning.

At cognee, we've found that combining these two technologies results in a powerful gestalt system in which vector search provides the semantic breadth to find relevant information across diverse sources, while the knowledge graph provides the structured precision to understand specific entities and their relationships.

For example, when answering a question about "the impact of SpaceX on commercial space flight," vector search might retrieve relevant passages about launch costs and technological innovations, while the knowledge graph provides specific facts about SpaceX's founding, key personnel, major launches, and relationships with NASA and competitors.

Misconception #6: Knowledge Graphs Are Just for Storing Facts

Many people view knowledge graphs primarily as repositories for factual information. Beyond storing facts, however, the true strength of knowledge graphs lies in their ability to represent complex relationships between the ingested data points.

A well-designed knowledge graph serves as a network of interconnected information that can be traversed and analyzed to uncover new insights. This enables:

• Inference: Deriving new knowledge from existing relationships
• Pattern recognition: Identifying recurring structures or anomalies
• Contextual understanding: Situating facts within their broader relationships
• Counterfactual reasoning: Exploring hypothetical scenarios by modifying the graph

For example, a knowledge graph might not explicitly state that "Alice and Bob are colleagues," but it could contain information that both Alice and Bob work at the same company in the same department. A query engine can traverse these relationships to infer the colleague relationship, even though it wasn't directly stored.

Misconception #7: Knowledge Graphs Solve All AI Memory Problems

With growing need to enhance the memory of AI systems, knowledge graphs are often portrayed as the perfect solution. While they do provide an excellent foundation, creating effective AI memory involves more than just storing information in a graph.

Knowledge graphs work brilliantly when the data remains static. However, real-world data is most often dynamic—it constantly evolves, requiring regular updates, careful curation, and precise organization.

Historically, the amount of manual effort involved in maintaining knowledge graphs has prevented their wider adoption. Anyone who’s worked in an organization where data isn't the main focus (and sometimes even in those where it is) knows that data management quickly becomes messy, complicated, and prone to entropy.

Data inevitably changes, becomes outdated, or gets misplaced. Keeping it accurate and up-to-date has traditionally meant an ongoing struggle against chaos. Thankfully, the emergence of LLMs has begun to automate many of these labor-intensive processes, significantly easing the burden.

The reality is that you don't just need a knowledge graph—you need a dynamic tool capable of efficiently loading, updating, managing, and evolving your data. That's exactly what cognee is.

How to Build Effective Knowledge Graphs?

With all these common misconceptions cleared up, the key question users often ask us is: how do I actually start building a knowledge graph?

Here are some guiding principles cognee embodies:

1. Start with clear use cases: Define what questions your knowledge graph needs to answer before deciding what data to include.
2. Design a flexible ontology: Create an entity and relationship model that captures the essential structure of your domain while remaining adaptable to new information.
3. Build deterministic and non-deterministic graph enrichment steps: First, use traditional approaches to clearly define your data. Then, introduce LLM-driven methods for more advanced enrichment—keeping in mind the old adage “garbage in, garbage out.”
4. Combine with complementary technologies: Use vector embeddings for semantic search, traditional databases for transactional data, and knowledge graphs for structured relationships.
5. Implement effective curation mechanisms: Whether using cognee’s automated processes or building your own, establish effective procedures for data insertion, deletion, and management.
6. Think beyond storage: Design systems for inference, reasoning, and contextual retrieval that leverage the graph structure—don’t just build a graph for the sake of building a graph.

Turning Knowledge Graphs into Engines of Insight

Hopefully, this post has made it clear that knowledge graphs are not merely a data storage technology, but more thinking tools—dynamic structures designed to enhance both machine and human understanding by catalyzing deeper reasoning and insight.

Here at cognee, our vision is big—building a smarter system that is able to transform the complexity of unstructured data into the clarity of meaningful insight. We are fully committed to creating practical, impactful solutions that clean up data, evolve graphs, and make effective use of the tools we already have.

If that sounds like something you need, join the conversation on our Discord channel or book a consultation with us for 1-on-1 guidance or support—we'd be more than happy to help.