Business Value of Ontologies¶

Ontologies provide tangible benefits for organizations building knowledge graphs. This page explains the return on investment and practical advantages.

The Problem: Unstructured Extraction¶

When extracting entities from text without guidance, LLMs make inconsistent decisions:

Input text:

"TAP Air Portugal flight TP1234, operated by an Embraer ERJ-195, experienced turbulence near Barcelona."

Without ontology (different runs may produce):

Run 1: Airline("TAP Air Portugal"), Plane("ERJ-195"), City("Barcelona")
Run 2: Operator("TAP"), Aircraft("Embraer ERJ-195"), Location("near Barcelona")
Run 3: Company("TAP Air Portugal"), Vehicle("ERJ-195"), Place("Barcelona area")

With ontology (consistent every time):

Operator("TAP Air Portugal"), Aircraft("Embraer ERJ-195"), Airport("Barcelona")

Key Benefits¶

1. Reduced Entity Fragmentation¶

Without ontology:

Graph contains:
- "TAP Air Portugal" (3 mentions)
- "TAP" (2 mentions)
- "TAP Portugal" (1 mention)
- "Air Portugal" (1 mention)

With ontology:

Graph contains:
- Operator: "TAP Air Portugal" (7 mentions, normalized)

Impact: 75% reduction in duplicate entities, cleaner queries, accurate counts.

2. Consistent Relationship Types¶

Without ontology:

Edges extracted:
- flies_for, operated_by, works_with, employed_at,
- flown_by, airline_of, carrier_for...

With ontology:

Edges extracted:
- HAS_OPERATOR (standardized)

Impact: Queries work reliably. MATCH ()-[:HAS_OPERATOR]->() finds all operator relationships.

3. Domain Coverage Guarantee¶

An ontology ensures important concepts aren't missed:

ECCAIRS Ontology Defines	Extraction Captures
Occurrence	Every incident
Aircraft	Every plane involved
Flight Phase	When it happened (takeoff, cruise, landing)
Primary Cause	Root cause analysis
Contributing Factors	Secondary causes

Without the ontology, an LLM might miss "Flight Phase" entirely because it doesn't know it's important.

4. Cross-Source Integration¶

When ingesting data from multiple sources:

Source: EASA Report     →  Occurrence, Aircraft, Operator
Source: News Article    →  Occurrence, Aircraft, Operator
Source: Internal DB     →  Occurrence, Aircraft, Operator
                              ↓
                        Unified Graph

The ontology acts as a common language enabling data fusion.

ROI Calculation¶

Time Savings¶

Task	Without Ontology	With Ontology	Savings
Entity deduplication	8 hours/week	1 hour/week	87%
Query development	4 hours/query	1 hour/query	75%
Data quality fixes	12 hours/week	2 hours/week	83%
Cross-source mapping	2 days/source	2 hours/source	94%

Quality Improvements¶

Metric	Without Ontology	With Ontology
Entity precision	~70%	~95%
Relationship accuracy	~60%	~90%
Query recall	~65%	~90%
False positive rate	~25%	~5%

When to Use an Ontology¶

Strong Fit¶

Regulated domains: Aviation, healthcare, finance (standards exist)
Multi-source integration: Combining data from different systems
High-stakes queries: Compliance, investigation, safety analysis
Long-term projects: Ontology investment pays off over time

May Not Need¶

Exploratory analysis: Quick one-off investigations
General knowledge: No domain-specific requirements
Single source: No integration challenges
Rapid prototyping: Speed over consistency

Ontology Investment Spectrum¶

No Schema ──────────────────────────────────────── Full Ontology
    │                                                    │
    ▼                                                    ▼
  Quick                                              Rigorous
  Flexible                                           Consistent
  Inconsistent                                       Integrated

        LLM-inferred    Hybrid    Graph-Hybrid
              │            │            │
              ▼            ▼            ▼
          Moderate     Balanced    Production
          effort       approach    quality

Aletheia supports the full spectrum via schema inference modes.

Case Study: Aviation Safety¶

Challenge: Analyze 10 years of incident reports from multiple authorities.

Without ontology: - 15 different ways to describe "turbulence" - Aircraft types inconsistently named - Causes scattered across free-text fields - 6 months to build usable graph

With ECCAIRS ontology: - Standard event classification - Normalized aircraft taxonomy - Structured cause-effect relationships - 2 weeks to build production graph

Result: 12x faster time-to-value, 40% more incidents correctly linked.

Case Study: Sanctions Compliance¶

Challenge: Track designated entities across US, UK, and Australian lists.

Without ontology: - Same organization under different names per jurisdiction - Alias relationships lost - No standard for "designated by" relationships

With FTM ontology: - Unified Organization type with aliases - SANCTION relationship to PublicBody - Cross-jurisdiction queries work immediately

Result: Single query answers "Is X sanctioned anywhere?" vs. three separate searches.

Summary¶

Benefit	Business Impact
Consistency	Reliable queries, accurate analytics
Integration	Unified view across sources
Coverage	No important concepts missed
Quality	Higher precision, lower error rates
Speed	Faster development, less rework
Compliance	Audit-ready, standards-aligned

The upfront investment in ontology selection and configuration pays dividends throughout the project lifecycle.