Imagine you're a detective walking into a crime scene.
On the table, you find a set of fingerprints. On the wall, a note was written in a familiar style.
With Vector Search, you can instantly compare these prints and handwriting against thousands of records to find things that look alike. It is fast, pattern-driven, and perfect for spotting similarities.
However, similarity alone doesn’t solve the case. You also need to know:
- Whose fingerprints are these?
- How is this suspect connected to the victim?
- Were they in the same vicinity at the same time?
- What motive links them into the bigger story?
That is where a knowledge graph comes in. The case file that links suspects, places, evidence, timelines and relationships. It does not just match patterns, it builds meaning, context and reasoning.
When you combine the two, you get the best of both worlds.
Vectors find hidden clues you may have missed, and knowledge graphs connect those clues into a story you can trust.
And in that sense, a detective's work is simply a branch of root cause analysis!
The Case for Operators
In the telco world:
- Vector databases can surface similar alarms and resolution paths, traffic anomalies and logs, performance degradation and trouble tickets.
- Knowledge graphs know how these anomalies impact services, customers and operations.
Just like a detective, operators do not want a myriad or ‘storm’ of clues, they want the full case solved. And improved Mean Time to Resolution (MTTR) means more cases can be solved quicker with fewer detectives.
A Vector database may surface two alarms on different routers that appear similar, but the knowledge graph shows that only one of them impacts the VoIP service for enterprise customers.
Automation in networks is like solving crimes faster. Autonomous networks go further, preventing crimes from even happening in the first place! But before we get there, we need to clean up and connect all the data points that we have. This is where data quality becomes foundational, otherwise, false positives and false accusations will occur.
Structured vs Unstructured Data
Not all evidence appears the same, and neither does all data. Structured and Unstructured data are different, and for AI to have the best effect, it needs to be managed using different methodologies.
Unstructured Data (documents, logs, chat transcripts, alarms text, even images) is messy and does not come with predefined meaning. This is where Vector databases shine. They take that messy content and embed it into a multi-dimensional space.
Think of an X,Y coordinate chart. In a simple X,Y chart, each dot has a coordinate. In reality, embeddings live in hundreds of dimensions, but the principle is the same, each piece of data gets a ‘location’ based on its meaning.
When you ask a question in natural language, GenAI turns your words into a Vector too, and then the database finds the points nearby. Essentially, it matches ‘things that look alike’. However, if there is no truly close match, the AI may still select the nearest neighbour, even if it is incorrect. That is one way that ‘hallucinations’ creep in, the system guesses from the closest point in space, even if the meaning does not quite fit.
Structured Data (Network topology, service hierarchies, customer records or configuration tables) comes with defined entities and relationships. It is not just ‘words on a page’, it has meaning within its structure.
This is where Knowledge graphs shine. They do not just store data points, they store connections, the ‘who, what and how’ between them.
It is like a map of relationships. Instead of a floating dot in space (like a Vector), every node in a graph is tied to others by meaningful links. For example:
- A router carries a service.
- A service belongs to a customer.
- An alarm is raised on a device.
These relationships form a web of context.
When an AI system asks a question, the graph does not just say “this looks similar.” It can reason, e.g., if router ‘x’ fails, it impacts this service, which affects this customer.
Graphs store the actual relationships. They provide the AI with facts and minimize hallucinations. Instead of guessing the closest match, it follows the connections to the correct cause and effect.
Implicit vs Explicit Knowledge
Another way to think about the difference is implicit vs explicit knowledge.
Vector databases work with implicit meaning.
They don’t store rules or connections directly. Instead, they capture patterns of similarity hidden in the data. For example, the words ‘fingerprint’ and ‘suspect’ end up close together in vector space because they appear in the same reports. The meaning is there, but it is implied rather than stated outright.
Knowledge graphs work with explicit meaning.
They define entities and relationships openly. A fingerprint belongs to Suspect X. Suspect X knows Victim Y. Nothing is left to guesswork, the connections are declared, structured and can be reasoned over with confidence.
In fact, within a true Knowledge graph, this is exactly how information is stored in what is known as a ‘triple’, where you have:
- Subject – Fingerprint
- Predicate – belongs to
- Object – Suspect X
(I’ll go into this in more detail in another blog.)
Another important point is Provenance, i.e., knowing where the data comes from. For example: “A particular router belongs to Service A (from Inventory system X, updated yesterday).”
This ties into Epistemology, i.e., the study of knowledge itself, specifically what it is, how we know it and how we justify it as true. In practice, it is about being able to answer: “Why does the AI know what it does, and why did it give me this answer?”
This is clear to understand with a Knowledge graph, you can trace the answer back through explicit links and sources. However, it is a little opaque with a Vector database, where mathematical processes and algorithms dictate why a decision was made.
Knowledge graphs bring traceability and explainability, you can see not just the result but also where it came from and why. Vector databases, in comparison, give you powerful pattern-matching, but with less transparency.
Solving the Case
So, back to the detective's case. Vectors are like the forensic analysts, “this fingerprint looks like that one.”
Graphs are like detectives , “this suspect with the fingerprint also had motive, opportunity and connection to the victim.”
Only by combining the two can you solve the case and find the root cause.
The conversation is not Vector databases vs Knowledge Graphs. It is about driving AI using Vector Databases & Knowledge Graphs.
Patterns vs Motives. Clues vs Connections. Together, they deliver recall and reasoning, the foundation of trusted AI in Telecom.
