Vector Databases & Triple Stores

The AI boom has brought a new class of databases into the spotlight: vector databases. But in the background triple stores have been quietly powering knowledge graphs and the Semantic Web for over two decades.

At one of my previous gigs I spent a few years working with teams building triple stores. I was helping advise on some aspects of the overall solution, but from the start I had my concerns. I loved the rigor, the formality and the way everything was connected. What I didn’t love was actually using the models. If you’ve ever used SparQL, you’ll know what I mean. It’s hard going. And then when we spoke to clients the most common question was “…how do we get this into a CSV…”.

And that’s a problem. A build it and they will come mentality, driven by academic purity.

It was a failure. Lessons were learned.

Fast forward a decade and I stated thinking about combining these two database types. Creating  hybrid AI systems that not only recall relevant information but can also reason about it.

Two Different Ways of “Knowing”

Vector Databases: Statistical Memory

A vector database store embeddings, these are high-dimensional numerical representations of the data (text, images, audio, video). In the loosest sense of the word this allows fuzzy matching – finding items that are similar to a query, even if the exact words or identifiers differ.

The knowledge here is sub-symbolic. Its distributed across the vector space. It exists in probability space, with no explicit concrete fact about why items are related.

Triple Stores: Symbolic Memory

A triple store stores explicit facts (subject, predicate, object) form, typically using RDF (Resource Description Framework). It’s absolute and deterministic. They excel at reasoning and logical inference

Example:

• (“Paris”, “isCapitalOf”, “France”)

• (“France”, “isIn”, “Europe”)

A SPARQL query can follow relationships to conclude that Paris is in Europe, even if that fact was never directly stored.

PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX dbr: <http://dbpedia.org/resource/>

SELECT ?city ?country
WHERE {
  ?city a dbo:City ;
        rdfs:label "Paris"@en ;
        dbo:country ?country .
}

PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX dbr: <http://dbpedia.org/resource/>

SELECT ?city ?country
WHERE {
  ?city a dbo:City ;
        rdfs:label "Paris"@en ;
        dbo:country ?country .
}

Why Combine Them?

On their own:

• A vector DB can find “nearby” concepts but can’t explain why they’re related.
• A triple store can explain relationships but can’t match fuzzy queries that use different words.

Together:

• Vector DB = Recall: “Here are the 10 most semantically relevant documents.”
• Triple Store = Reason: “Given these documents, here’s how they connect and the conclusion you can draw.”

This is the neuro-symbolic approach to AI — blending statistical learning with symbolic logic.

Worked Example: Hybrid RAG System

Let’s imagine we’re building an AI assistant for pharmaceutical research.

Step 1 — Vector Search for Semantic Recall

We store research papers’ embeddings in Pinecone.
A query for “ACE2 inhibitor for viral infection” retrieves:

1. Paper A — Mentions “angiotensin-converting enzyme 2 blockers in coronavirus studies”
2. Paper B — Discusses “virus entry and ACE2 receptors”
3. Paper C — Mentions “antiviral compounds targeting ACE2”

Step 2 — Triple Store for Reasoning

In GraphDB, we have explicit biomedical facts:(

(ACE2, bindsTo, SARS_CoV_2_SpikeProtein)
(ACE2Inhibitor, inhibits, ACE2) 
(SARS_CoV_2_SpikeProtein, enables, ViralEntry)

From these, the triple store can infer:

• ACE2 inhibitors may block viral entry.

Step 3 — Putting It Together

1. Vector DB finds relevant papers — even if they don’t use the same terminology.
2. Triple Store checks explicit biological pathways to confirm whether the mechanism is scientifically plausible.
3. AI assistant outputs: “Papers A, B, and C discuss ACE2 blockers. Based on known biological interactions, these inhibitors could potentially block viral entry via the spike protein.”

When Each Shines

• Use a Vector DB when:
  • Searching large, unstructured corpora (text, images, audio)
  • You need “find me something like this” queries
  • You’re supporting LLM-based RAG
• Use a Triple Store when:
  • You need explainable answers
  • You’re modeling complex, multi-step relationships
  • Compliance, provenance, and traceability matter

The Future is Hybrid?

Maybe. Just as relational databases didn’t replace file systems, vector DBs won’t replace triple stores, but they can work together.

Expect to see:

• Vector search inside triple stores (hybrid queries in SPARQL + ANN)
• Triple store reasoning layers on top of vector DB recall

The next generation of AI systems will be both fuzzy and precise, just like human memory.