Ontology & Knowledge Graph Study

Concepts

What Are Ontology & Knowledge Graphs?

Two core concepts for giving data "meaning"

O

Ontology

A formal definition of concepts and relationships in a domain. "Customers place orders, orders contain products, products belong to categories" — expressed in a machine-readable format. An ontology is a schema — it defines the structure and rules of data, not the data itself.

K

Knowledge Graph

Actual data connected as nodes and edges based on an ontology. If the ontology says "customers place orders," the knowledge graph says "John placed Order #1234." A knowledge graph contains instance data.

Simply put: An ontology is the column headers of an empty spreadsheet (schema). A knowledge graph is the actual data filled in. But unlike a spreadsheet, data is represented as a graph (nodes and edges), so complex relationships are naturally expressed.

≠

How Is This Different from a Relational DB?

Relational DBs express relationships through tables and JOINs. As relationships deepen, JOINs get complex and performance drops. Knowledge graphs store relationships as first-class citizens — finding "3-hop relationships" is natural and fast. When AI needs "categories of products bought by John's colleagues," graph traversal beats 5 JOINs.

∴

Why Does This Matter for AI?

Benchmarks show: LLM without knowledge graph = 16.7% accuracy. With knowledge graph = 56.2% — a 3.4x improvement. On complex queries with 10+ entities, vector search accuracy drops to 0% while graph-based stays above 70%.

Spectrum

Ontology Spectrum: Simple to Complex

You don't need OWL on day one — add depth incrementally

Glossary

A list of terms with definitions

Starting Point

Taxonomy

Hierarchy (is-a). e.g. Animal > Mammal > Dog

Classification

Thesaurus

Synonyms, related terms, broader/narrower

Relations

Ontology

Properties, constraints, logical rules, inference

Reasoning

Practical tip: Production experience shows 3–7 node types and 5–15 relationship types is optimal. A simple 5-class ontology with 10 well-defined properties extracts more reliably than a 50-class deep hierarchy. More complex ≠ better.

Building Blocks

Triples: The Atomic Unit of Knowledge Graphs

Every knowledge graph is built from Subject-Predicate-Object triples

John (Subject)

→

placed (Predicate)

→

Order #1234 (Object)

Subject (who) → Predicate (did what) → Object (to what)

Order #1234

→

contains

→

MacBook Pro

→

belongs to

→

Laptop Category

Chain triples to form a graph — "John → Order → MacBook Pro → Laptop Category"

This is fundamentally different from relational DBs. To find "the category of products John ordered" in SQL, you JOIN customers → orders → order_items → products → categories. In a knowledge graph, you simply traverse nodes. The deeper the relationship, the more dramatic this advantage becomes.

GraphRAG

GraphRAG: Knowledge Graph + RAG

If vector search hit its limits — graphs may be the answer

Multi-hop Reasoning (Complex Questions) GraphRAG 86% vs Vector 32%

GraphRAG 86%

Vector 32%

Numerical Reasoning GraphRAG 100% vs Vector 50%

GraphRAG 100%

Vector 50%

Aggregation Queries (Schema-bound) GraphRAG 90% vs Vector 0%

GraphRAG 90%

Vector 0%

Simple Semantic Search (Find Docs) Comparable — graph adds overhead only

GraphRAG ~equal

Vector ~equal

Sources: FalkorDB, TianPan, Lettria

Query Type	Vector RAG	GraphRAG	Recommendation
"Find docs about topic X"	Good fit	Overkill	Vector
"What's the relationship between A and B?"	Insufficient	Good fit	Graph
"Total sales of X last month?"	Can't do	Good fit	Graph
"Path of influence from A to B?"	Can't do	Good fit	Graph
"Summarize latest papers on this topic"	Good fit	Unnecessary	Vector

The 80/15/5 rule: 2026 benchmark consensus shows ~80% of enterprise queries are simple semantic search (Vector), ~15% need structured reasoning (Graph), ~5% need full agentic treatment. It's not either/or — a hybrid router is the answer.

Tools

Tool Ecosystem

Tools for building your own knowledge graphs

Graph DB

Neo4j

Most widely used graph DB. Cypher query language, desktop app for quick start. "Ontologies as a First-Class Citizen" on 2026 roadmap.

Cypher · Java · Largest community

Graph DB

FalkorDB

Real-time AI-optimized graph DB. Sparse matrix multiplication for ultra-low latency traversals. Runs as Redis module. GraphRAG SDK for auto ontology generation.

C · Redis Module · One-line Docker start

Framework

Graphiti (by Zep)

Temporally-aware knowledge graph framework. AI agent memory specialized. Supports Neo4j, FalkorDB, Amazon Neptune. 45k+ GitHub stars.

Python · Multi-agent · Real-time

Framework

LangChain + LangGraph

Build GraphRAG pipelines in the LangChain ecosystem. Neo4j, FalkorDB integration. Supports vector + graph hybrid search.

Python/JS · Broadest integrations

Platform

TrustGraph

The Context Operating System. OntologyRAG support — automatically builds and manages ontology-based context graphs.

Open source · OntologyRAG

Platform

GraphRAG SDK (FalkorDB)

Auto-detect ontologies and generate knowledge graphs from unstructured data. Manual and automatic ontology management.

Python · Auto ontology · Production-grade

Practice

Getting Started: A Step-by-Step Approach

For first-time ontology builders

1

Start from Your DB Schema

Research shows ontologies extracted from DB schemas perform comparably to text-derived ones, at far lower cost. Feed DDL (table definitions) to an LLM to auto-extract classes, properties, and relationships. Leverage existing data structure.

2

Start Small

Begin with 3–7 node types and 5–15 relationship types. A precise 5-class ontology beats a perfect 50-class one. Expand incrementally as needs arise.

3

Go Hybrid

No need to abandon vector search for graphs. 80% of queries work fine with vector search. Use graphs for the 15% requiring complex relationship reasoning. Let vectors handle the rest.

4

Invest in Entity Resolution

The biggest issue in early GraphRAG: "John Doe, 45" vs "John Doe, age 45", "Type 2 Diabetes" vs "T2D". If the same entity has different names, the graph breaks. Synonym dictionaries and normalization are essential.

ROI reference: 2024–2025 production cases show organizations adopting knowledge graphs achieved 300–320% ROI. But this is for orgs with ready data. Data structuring (AI-Ready Data) comes first.