What Is an Application Knowledge Graph and How Does It Make Enterprise Data AI-Ready?
Solix Technologies Announces General Availability of Data Sense and Data Ask: The Bridge From AI-Ready to AI-Activated Data highlights a fundamental challenge in enterprise AI: organizations have enormous amounts of data, but much of its business meaning remains hidden inside complex applications, schemas, and institutional knowledge. An Application Knowledge Graph can help solve this problem by mapping application structures, relationships, business concepts, and query patterns so AI systems can understand enterprise data in context. Solix Data Sense positions its Application Knowledge Graph (AKG) as a capability that maps schemas and discovers undeclared relationships, transforming previously implicit knowledge into a semantic layer that Data Ask can query.
What Is an Application Knowledge Graph?
An Application Knowledge Graph is a structured representation of the entities, relationships, business meanings, schemas, and application-specific knowledge contained within enterprise systems.
In simple terms, an AKG creates a map of how an enterprise application works.
Consider an ERP system containing thousands of tables.
A traditional AI system might see:
Customer table
Order table
Invoice table
Product table
Payment table
But simply seeing the tables does not necessarily tell AI how they are connected.
An Application Knowledge Graph can represent relationships such as:
Customer → places → Order
Order → contains → Product
Order → generates → Invoice
Invoice → receives → Payment
This creates a machine-readable representation of business context.
Why Does Enterprise AI Need an Application Knowledge Graph?
Enterprise applications are often much more complicated than demonstration datasets.
Why Are Enterprise Schemas Difficult for AI?
Large applications can contain:
Thousands of tables
Complex relationships
Cryptic column names
Legacy naming conventions
Custom fields
Application-specific business logic
Undocumented relationships
A generic AI system looking only at raw schema information may have difficulty determining what those structures actually mean.
What Happens When AI Does Not Understand Application Context?
It may generate a query that looks technically correct but produces the wrong business result.
This is particularly dangerous because the answer may appear plausible.
Solix describes this problem directly in its current Data Ask positioning: generic tools can struggle with complex enterprise applications such as Oracle, SAP, and PeopleSoft because raw schemas do not adequately communicate business meaning.
How Does an Application Knowledge Graph Work?
An AKG connects technical application structures with business meaning.
What Information Can an AKG Represent?
An Application Knowledge Graph can capture:
Tables
Columns
Entities
Relationships
Business terms
Application structures
Query patterns
Business processes
Semantic relationships
This creates a richer representation than a traditional database schema.
What Is the Difference Between a Schema and an AKG?
A schema tells you how data is structured.
An AKG helps explain what that structure means and how the components relate.
For example:
Schema
customer_id
order_id
product_id
AKG
Customer → places → Order → contains → Product
The second representation provides much more business context.
Can an Application Knowledge Graph Discover Undeclared Relationships?
Yes.
This is one of the important capabilities Solix associates with Data Sense.
What Are Undeclared Relationships?
An undeclared relationship is a meaningful connection between data elements that may not be explicitly documented.
For example:
Customer Number
might connect to:
Billing Account
even if the relationship is not clearly described in documentation.
An AKG can help identify these connections based on application structure and patterns.
Solix describes Data Sense's AKG as mapping application schemas and discovering undeclared relationships, turning implicit or tribal knowledge into a semantic layer that Data Ask can query.
Why Is Tribal Knowledge a Problem for Enterprise AI?
Many enterprise applications contain knowledge that exists primarily in people's heads.
What Is Tribal Knowledge?
Tribal knowledge is information understood by experienced employees but not formally documented in systems or documentation.
For example, an experienced ERP specialist may know:
"This field doesn't actually represent the customer's name; it represents the account hierarchy."
That knowledge may be essential for correctly interpreting the data.
What Happens When Experts Leave?
Organizations can lose valuable application knowledge.
This creates challenges for:
Application modernization
Data migration
Application retirement
Analytics
AI initiatives
Compliance
An Application Knowledge Graph can help capture some of this application-specific knowledge in a machine-readable structure.
How Does an Application Knowledge Graph Create a Semantic Layer?
The semantic layer connects technical information with business meaning.
What Does the AKG Add to a Semantic Layer?
It can connect:
Tables
↓
Columns
↓
Entities
↓
Business Terms
↓
Relationships
↓
Business Processes
This allows AI applications to reason about enterprise information in a more meaningful way.
Why Is This Important for AI?
AI needs to understand not just individual values, but the relationships between those values.
For example:
Customer
is related to
Account
which is related to
Order
which is related to
Invoice
This relationship context can be essential when answering complex business questions.
How Does an Application Knowledge Graph Support Natural-Language Queries?
Natural-language querying is one of the strongest use cases for an AKG.
What Is Natural-Language Data Querying?
Instead of asking a technical user to write SQL, a business user can ask:
"Which customers generated more than $1 million in revenue last year?"
The AI system must determine:
What is a customer?
What counts as revenue?
Which source should be queried?
What does "last year" mean?
Which tables contain the required information?
The AKG provides application-specific context for these decisions.
How Does AKG Improve NL2SQL?
NL2SQL converts natural-language questions into SQL queries.
Why Does Generic NL2SQL Struggle With Enterprise Applications?
Generic NL2SQL systems may rely heavily on:
Table names
Column names
Schema structure
That approach can work with simple databases.
But complex enterprise systems can contain cryptic structures and undocumented relationships.
What Does AKG-Based NL2SQL Do Differently?
Instead of asking AI to guess what the schema means, the application knowledge graph provides contextual information.
Solix describes Data Ask as generating SQL based on its Application Knowledge Graph rather than deriving meaning only from the raw schema.
This creates a more grounded approach to natural-language enterprise queries.
Can an Application Knowledge Graph Improve AI Accuracy?
It can improve the contextual foundation used by AI, although it does not guarantee that every AI response will be correct.
Why Does Context Matter?
Suppose an ERP contains:
AMT
That could mean:
Invoice amount
Tax amount
Discount amount
Payment amount
A generic AI model may need to infer the meaning.
An AKG can associate the field with the relevant business concept and application relationship.
This reduces dependence on guesswork.
Is This Important for Enterprise AI?
Yes.
Enterprise AI requires a much higher standard of accuracy than casual consumer AI because outputs may influence:
Financial decisions
Operations
Compliance
Customer service
Supply chains
Healthcare processes
How Does Application Knowledge Graph Support SAP and Oracle Data?
Enterprise applications such as SAP and Oracle can contain highly complex schemas.
Why Are SAP and Oracle Systems Difficult for AI?
They often contain:
Large numbers of tables
Complex relationships
Technical naming conventions
Multiple business modules
Customizations
Legacy structures
Generic AI systems may not understand these structures without additional application context.
Can Prebuilt Application Knowledge Graphs Help?
Yes.
Solix currently states that preconfigured AKGs are available for Oracle E-Business Suite and SAP ECC/S/4HANA, while custom AKGs can be built for other applications through Data Sense Builder.
This can reduce the amount of manual semantic modeling required for common enterprise applications.
Does an Application Knowledge Graph Require Data Migration?
Not necessarily.
Does the Data Have to Move Into a New Platform?
An important advantage of an application-knowledge approach is that the knowledge layer can be created around existing enterprise data.
Solix states that Data Ask can query managed enterprise databases without requiring data migration, with data remaining at its existing location.
This can be valuable for enterprises that have:
Large databases
Legacy systems
Regulatory constraints
Data residency requirements
Complex migration projects
How Does AKG Support AI-Ready Data?
AI-ready data requires context.
What Does AKG Add?
An Application Knowledge Graph can provide:
Application semantics
Relationships
Business terms
Schema understanding
Query patterns
Business context
This can transform raw enterprise data into information that is easier for AI applications to interpret.
Can AKG Work With Metadata?
Yes.
Metadata describes enterprise data.
An AKG can build on that information by representing relationships and semantic meaning.
Together:
Metadata + Semantic Layer + Knowledge Graph
can provide a stronger foundation for AI-ready enterprise information.
How Does AKG Connect to Data Sense?
Data Sense is positioned by Solix as the intelligence layer that prepares enterprise information for AI.
Its three key capabilities are:
Application Knowledge Graph
Content Intelligence
Intelligent Classification
Solix describes the AKG as mapping application schemas and discovering undeclared relationships, while the other capabilities address enterprise documents and classification.
Why Combine These Capabilities?
Enterprise knowledge exists in different forms.
Structured data needs application and schema understanding.
Unstructured documents need content intelligence.
Sensitive and governed information needs classification.
Combining these capabilities provides a broader AI-ready intelligence layer.
Data Ask represents the activation side of the architecture.
What Does Data Ask Do With AKG?
The Application Knowledge Graph provides the contextual foundation for Data Ask's structured data queries.
A user can ask a question in natural language.
The system can then:
Understand the question
↓
Interpret enterprise terminology
↓
Use the AKG to understand relationships
↓
Generate the appropriate query
↓
Execute against the managed data
↓
Return an answer
Solix describes Data Ask as using the AKG to encode data meaning, relationships, business terms, and tested query patterns.
Can an Application Knowledge Graph Support Cross-Application Queries?
Yes, and this is an important enterprise use case.
Why Are Cross-Application Queries Difficult?
An organization might store customer information in CRM, orders in ERP, and contracts in a document repository.
A business question may require information from all three.
For example:
"Which customers with contracts expiring this year generated more than $500,000 in orders last year?"
This could require:
CRM + ERP + Contract Documents
A unified application and semantic understanding can help connect those sources.
Solix's Data Ask architecture describes cross-application querying as a capability for combining information across enterprise systems into a single answer.
How Does AKG Support Hybrid AI Queries?
Enterprise questions are not always purely structured or unstructured.
What Is a Hybrid Query?
A hybrid question requires both database information and document information.
For example:
"Which customers purchased Product X and are affected by the latest contract policy?"
The system may need:
Structured data
for customer purchases.
and
Unstructured data
for the contract policy.
Why Is This Important?
A modern enterprise AI architecture should not force users to decide whether a question belongs to a database or document repository.
Solix's current Data Ask architecture describes structured, unstructured, and hybrid access through a single interface.
How Does AKG Support Enterprise RAG?
Enterprise RAG needs reliable context.
Why Is Enterprise RAG Different From Basic RAG?
A simple RAG application might retrieve chunks from a small collection of documents.
Enterprise RAG can involve:
Multiple repositories
Sensitive information
Structured databases
Complex permissions
Business terminology
Large document collections
An Application Knowledge Graph can provide additional structured context around enterprise applications.
Can AKG Work Alongside Document RAG?
Yes.
The AKG can provide context for structured application data, while RAG can retrieve relevant unstructured content.
A hybrid architecture can therefore connect:
Structured Enterprise Data + Unstructured Enterprise Knowledge
How Does AKG Support AI Governance?
AI needs to understand not only what data means, but also how it should be used.
What Governance Information Matters?
Organizations may need to understand:
Data ownership
Permissions
Classification
Sensitivity
Source
Business purpose
Audit requirements
An Application Knowledge Graph can contribute application context to this broader governance architecture.
Does AKG Replace Data Governance?
No.
AKG is an intelligence and semantic capability.
Organizations still need:
Access controls
Security policies
Privacy controls
Governance frameworks
Compliance processes
The graph should work within those controls.
Can an Application Knowledge Graph Help With Legacy Data?
Yes.
Legacy systems often contain valuable information but are difficult to understand.
Why Is Legacy Data Difficult for AI?
Legacy applications may have:
Outdated documentation
Complex schemas
Obsolete naming conventions
Undocumented relationships
Limited technical expertise
An AKG can help capture application knowledge and make legacy information more accessible.
Can This Support Application Retirement?
Yes.
Application retirement does not necessarily mean losing access to business information.
Solix's enterprise data preservation content describes the Application Knowledge Graph as enabling natural-language queries against complex enterprise schemas, including Oracle EBS and SAP environments.
This creates a path toward preserving data while reducing dependency on legacy applications.
How Can AKG Help With ERP Modernization?
ERP modernization projects often involve complex data mapping.
What Problems Occur During ERP Modernization?
Organizations need to understand:
Existing schemas
Data relationships
Business processes
Historical information
Custom fields
Reporting dependencies
An Application Knowledge Graph can help expose application relationships and business context before modernization.
Can This Reduce Modernization Risk?
Better understanding of existing data can help teams make more informed decisions about:
Migration
Archiving
Data transformation
Application retirement
Integration
Can AKG Preserve Institutional Knowledge?
Yes, this is one of its strategic benefits.
Why Is Institutional Knowledge Important?
Employees may understand:
Why a field exists
Which tables are authoritative
How applications relate
Which reports depend on certain data
Which business rules apply
If that knowledge is never documented, organizations can lose it.
An Application Knowledge Graph provides a way to encode some of this knowledge into a reusable semantic representation.
What Are the Benefits of an Application Knowledge Graph?
Can AKG Improve Enterprise Data Discovery?
Yes. It can help organizations understand relationships across application structures.
Can AKG Improve AI Readiness?
Yes. It provides application-specific context and semantics.
Can AKG Improve Natural-Language Queries?
Yes. It gives AI additional context for interpreting business questions.
Can AKG Support NL2SQL?
Yes. It can provide the semantic foundation for translating business questions into application-aware SQL.
Can AKG Reduce Dependence on Tribal Knowledge?
It can capture application relationships and business context that might otherwise remain with individual experts.
Can AKG Support Legacy Modernization?
Yes. It can help organizations understand complex application data before migration or retirement.
What Challenges Should Enterprises Consider Before Implementing AKG?
An Application Knowledge Graph is powerful, but implementation still requires planning.
What About Data Quality?
An AKG cannot fix fundamentally incorrect source data.
What About Changing Applications?
Enterprise applications evolve.
The knowledge graph needs to remain aligned with changes in schemas and business processes.
What About Governance?
Access and security policies must continue to apply.
What About Human Validation?
AI-generated mappings and relationships may require review.
Solix's current Data Sense approach emphasizes a guided, ten-stage workflow with human review at each stage before publishing AI-ready results.
How Should Enterprises Build an Application Knowledge Graph?
Organizations can approach AKG implementation in stages.
What Is the First Step?
Identify priority applications.
Start with applications where AI access would create measurable value.
What Is the Second Step?
Map application schemas.
Identify:
Tables
Columns
Entities
Relationships
What Is the Third Step?
Add business meaning.
Map technical structures to:
Business terms
Entities
Metrics
Processes
What Is the Fourth Step?
Validate relationships.
Use application experts and business users to confirm important mappings.
What Is the Fifth Step?
Connect governance.
Apply appropriate:
Permissions
Classification
Security
Compliance policies
What Is the Final Step?
Expose the knowledge to AI applications.
This can support:
Natural-language queries
AI assistants
RAG
Enterprise search
AI agents
What Is the Future of Application Knowledge Graphs?
Application Knowledge Graphs are likely to become increasingly important as enterprises move from AI experimentation to AI-driven operations.
The progression is:
Raw Enterprise Data
↓
Metadata
↓
Semantic Layer
↓
Application Knowledge Graph
↓
AI-Ready Data
↓
AI-Activated Data
↓
AI Agents
The important shift is that AI no longer needs to treat enterprise applications as collections of disconnected tables.
Instead, AI can work with a representation of the business meaning and relationships inside those applications.
Why Is Application Knowledge Graph Important for AI-Activated Data?
Preparing data for AI is only half the challenge.
Organizations also need to activate that data.
An Application Knowledge Graph can provide the contextual foundation that allows users to ask questions about enterprise applications using natural language.
This is the bridge between:
Data
and
Understanding
and finally:
AI Interaction
Solix's current Data Sense and Data Ask architecture reflects this progression: Data Sense builds the intelligence layer, while Data Ask puts that intelligence to work through natural-language access.
Frequently Asked Questions
What is an Application Knowledge Graph?
An Application Knowledge Graph is a structured representation of application schemas, entities, relationships, business terms, and application-specific knowledge that helps AI understand enterprise data.
What does AKG stand for?
AKG stands for Application Knowledge Graph.
How does an Application Knowledge Graph support AI?
It provides application-specific context, relationships, semantics, and business meaning that can help AI interpret enterprise data.
What is the difference between an Application Knowledge Graph and a database schema?
A database schema describes the technical structure of data. An Application Knowledge Graph adds relationships, business meaning, entities, and application context.
Can an Application Knowledge Graph improve NL2SQL?
Yes. AKG can provide application-specific semantics and relationships that help AI translate natural-language questions into more contextually appropriate SQL.
Can AKG work with SAP?
Yes. Solix currently states that preconfigured AKGs are available for SAP ECC and S/4HANA.
Can AKG work with Oracle?
Yes. Solix currently states that a preconfigured AKG is available for Oracle E-Business Suite.
Does an Application Knowledge Graph require data migration?
Not necessarily. Solix states that Data Ask can work with managed databases while data remains in its existing location.
Can AKG support legacy application modernization?
Yes. By mapping application structures and relationships, AKG can help organizations understand legacy data before migration, modernization, or application retirement.
How does Data Sense use Application Knowledge Graph?
Solix Data Sense uses AKG to map application schemas and discover undeclared relationships, turning application knowledge into a semantic layer that Data Ask can query.
How does Data Ask use AKG?
Data Ask uses the Application Knowledge Graph to understand enterprise application meaning, relationships, business terms, and query patterns when answering natural-language questions.
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