Semantic Context
The Semantic Context area enables Data Administrators to enrich CryspIQ® with organisation-specific terminology, business definitions and industry language.
Every organisation develops its own language over time.
Different teams often use:
- Business acronyms
- Industry terminology
- Technical jargon
- Internal project names
- Legacy system names
- Alternative names for the same business concept
The Semantic Context area allows these definitions to be captured and linked to the CryspIQ® Enterprise Data Model.
This improves:
- Data discovery
- Search relevance
- Natural Language Query accuracy
- AI-generated insights
- User adoption
Overview
Users often ask questions using business terminology rather than technical data model names.
For example:
| User Terminology | Actual Data Definition |
|---|---|
| Customer | Business Entity |
| Employee | Person |
| Product Line | Service |
| Revenue | Financial Transaction |
| Region | Location |
| Asset Register | Asset Master |
Without semantic context, users and AI models may struggle to identify the correct data.
Semantic Context bridges this gap by teaching CryspIQ® how your organisation speaks.
Why Semantic Context Matters
Traditional data platforms expect users to understand:
- Table names
- Database structures
- Technical terminology
CryspIQ® takes a business-first approach.
By maintaining a semantic knowledge layer, users can search using familiar language while CryspIQ® translates that language into the correct enterprise context.
How Semantic Context Works
Business User Question
↓
Semantic Context
↓
Enterprise Data Model
↓
Correct Data Identified
↓
Results Returned
Example:
Question:
Show customer revenue by region
Semantic Context understands:
Customer = Business Entity
Revenue = Financial Transaction
Region = Location
The correct query can then be generated automatically.
Benefits
The Semantic Context area helps organisations:
- Improve Natural Language Query results
- Improve AI response accuracy
- Increase search relevance
- Standardise business terminology
- Reduce ambiguity
- Improve user adoption
- Capture organisational knowledge
- Support onboarding of new staff
Managing Semantic Context
Navigate to:
Consume → Semantic Context
The Data Administrator can manage all semantic definitions used within the organisation.
Semantic Context Library
The library stores business definitions and terminology.
Examples include:
| Term | Definition |
|---|---|
| Customer | Organisation purchasing products or services |
| Revenue | Income generated from business activities |
| Asset | Physical or digital item owned by the organisation |
| Employee | Person employed by the organisation |
| Region | Geographic area of operation |
Creating a New Semantic Definition
Step 1 – Select Create
Choose:
New Semantic Definition
Step 2 – Enter the Business Term
Provide the term commonly used within the organisation.
Example:
Customer
Step 3 – Enter the Definition
Provide a clear business explanation.
Example:
A customer is an organisation or individual that purchases products or services from the business.
Step 4 – Add Alternative Terms
Add synonyms or related terminology.
Examples:
Client
Account
Consumer
Purchaser
These terms will be treated as equivalent concepts.
Step 5 – Link to Enterprise Context
Associate the term with the relevant CryspIQ® business concept.
Example:
Business Entity
This ensures queries are directed to the correct information.
Step 6 – Save
The new semantic definition becomes available immediately to search and Natural Language Query services.
Updating Definitions
Definitions can be updated at any time.
Common updates include:
- Business terminology changes
- New products
- New services
- Organisational restructuring
- Industry terminology changes
Keeping definitions current improves AI and user experience.
Removing Definitions
Definitions that are no longer required can be removed.
Before deletion:
- Verify the term is no longer used.
- Confirm it is not required by business users.
- Confirm it is not supporting AI query interpretation.
Examples of Semantic Context
Business Terminology
Customer
Client
Account
All may refer to the same business concept.
Industry Terminology
Mining Industry:
ROM
Ore Body
Crusher Feed
Stockpile
Healthcare:
Patient
Encounter
Episode
Provider
Financial Services:
Policy
Premium
Claim
Broker
Internal Terminology
Many organisations use internal project names.
Example:
Project Falcon
Project Horizon
Customer 360
These terms can be linked to formal business definitions.
Supporting Natural Language Query
The Semantic Context area significantly improves Natural Language Query accuracy.
Without semantic definitions:
Show me customer profitability
may produce uncertain results.
With semantic definitions:
Customer = Business Entity
Profitability = Financial Performance
CryspIQ® can generate more accurate SQL and return more relevant results.
Supporting AI Initiatives
Semantic Context also improves:
- AI assistants
- Machine Learning models
- Search functionality
- Recommendation engines
- Enterprise knowledge discovery
AI systems perform best when business meaning is clearly defined.
The Semantic Context library provides that meaning.
Best Practices
Use Business Language
Write definitions using terminology that business users understand.
Avoid overly technical language where possible.
Capture Acronyms
Include commonly used acronyms.
Example:
CRM
ERP
HRIS
GL
P&L
Include Synonyms
Capture alternative names for the same concept.
This improves search and AI interpretation.
Review Regularly
Review definitions periodically to ensure they remain accurate.
Align with Governance
Ensure terminology aligns with approved organisational definitions and governance standards.
Common Examples
Revenue
Revenue generated from the sale of products and services.
Synonyms:
Sales
Income
Turnover
Employee
Individual employed by the organisation.
Synonyms:
Staff Member
Worker
Team Member
Customer
Organisation or individual purchasing products or services.
Synonyms:
Client
Account
Consumer
Common Questions
Who can manage Semantic Context?
Typically Data Administrators and authorised governance users.
Does this change the data?
No.
Semantic Context enriches understanding of the data but does not modify underlying records.
Does this improve Natural Language Query?
Yes.
Semantic definitions help CryspIQ® interpret user questions more accurately.
Does this help AI?
Yes.
Semantic Context provides organisational knowledge that improves AI understanding and response quality.
Related Guides
Next Steps
- Review commonly used business terminology.
- Capture organisational definitions.
- Add synonyms and acronyms.
- Link definitions to enterprise context.
- Test Natural Language Queries.
- Refine definitions over time.
The Semantic Context library helps CryspIQ® understand how your organisation speaks, making data discovery, Natural Language Query and AI interactions significantly more accurate and valuable.