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A useful modernization knowledge graph connects business capabilities and rules to the code, data, APIs, integrations, infrastructure, owners, and evidence that implement them. Build it in this order: define the decisions you need to make, inventory the estate and its sources, design a small typed ontology, extract facts with provenance, validate high-impact relationships with experts, query the graph for options, and govern it as a living discovery pipeline. The graph is decision evidence—not an automatic architecture generator.
What a modernization knowledge graph does
Legacy systems are difficult to change because their important relationships are scattered. A business rule may be implemented in a class, duplicated in a batch job, constrained by a database trigger, exposed through an API, and depended on by an external system. A code index can find symbols, but it usually cannot answer which business capability a symbol supports or who owns the resulting data.
A modernization graph makes those connections explicit. It can represent business capabilities, requirements, and rules alongside applications, modules, classes, methods, APIs, tables, data stores, integrations, technologies, deployment assets, and bounded contexts. Edges such as IMPLEMENTS, READS_FROM, WRITES_TO, INVOKES, DEPENDS_ON, FOREIGN_KEY, INTEGRATES_WITH, and DEPLOYED_ON turn scattered facts into paths that can be inspected.
The result is not a guarantee that every dependency has been found. A missing edge can mean that a repository, configuration file, generated artifact, or operational record was unavailable, or that an extractor did not understand it. Every important conclusion therefore needs a source, a confidence level, and a review state.
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Start with decisions, not with a database
Write the modernization questions before choosing a graph engine or importing source code. AWS Prescriptive Guidance recommends beginning with business drivers, stakeholder requirements, an initial portfolio inventory, and a prioritized assessment plan. Questions that produce useful first iterations include:
- Which business capability depends on this application, program, API, or table?
- Which systems call this API, consume this message, or share this database object?
- Which components read or write the same data, and who is accountable for that data?
- What would be affected if an integration, schema, runtime, or vendor technology changed?
- Which parts of a monolith appear to form a bounded context, and what evidence contradicts that boundary?
- What must move together to preserve a business rule, transaction, security control, or service-level objective?
- Which modernization option offers acceptable business value, readiness, cost, and risk?
| Decision | Evidence the graph should connect | Useful output |
|---|---|---|
| Assess impact of changing a component | Incoming calls, outgoing calls, shared tables, messages, configuration references, deployment relationships | Dependency chain with confidence and affected owners |
| Find a candidate service boundary | Business capability, rules, data ownership, transaction paths, coupling, security and operational constraints | Candidate boundary plus supporting and conflicting evidence |
| Prioritize an application | Criticality, business drivers, compliance, technical risk, readiness, cost and dependencies | Portfolio assessment and sequencing recommendation |
| Plan a migration slice | Components, schemas, integrations, deployment assets, test evidence and owners in the proposed slice | Scope, prerequisites, risks and rollback dependencies |
Keep the first release narrow enough to answer a few consequential questions. An indiscriminate code index can become expensive to refresh while still failing to explain business meaning.
Inventory the estate and qualify its evidence
Create a portfolio record for every application
For each application, record its name, primary function, business capability, criticality, environment, product and version details, known dependencies, owners and stakeholders, major issues and risks, business drivers, and compliance context where available. Mark unknown fields explicitly. An empty value means “not yet established,” not “none.”
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Include infrastructure and operational context: deployment targets, schedulers, queues, certificates, secrets-management references, network zones, monitoring, batch windows, backup arrangements, and recovery objectives. These details often determine whether a seemingly isolated component can actually be moved.
List source systems and collection dates
Identify where each fact comes from: repositories, build outputs, database scripts and metadata, API specifications, integration definitions, configuration, infrastructure-as-code, deployment records, runbooks, tickets, monitoring, interviews, and architecture documents. Store the source identifier, version or commit, collection timestamp, and responsible team. AWS recommends progressive collection and repeated reassessment rather than waiting for a perfect dataset.
Prioritize machine-readable sources without treating them as infallible
AWS guidance says programmatic data is typically more trusted than written documentation, static data, and institutional knowledge. Use that as a prioritization principle: parse code, schemas, API definitions, and deployment manifests where possible, then reconcile the results with documentation and people. Generated code, reflection, dynamic SQL, runtime configuration, undocumented interfaces, and stale repositories can make automated extraction incomplete or wrong.
| Source | Facts commonly extracted | Typical qualification |
|---|---|---|
| Source code and build files | Classes, methods, imports, call sites, annotations, modules, language and version | Reflection, generated code, dynamic loading and conditional compilation may hide relationships |
| Database DDL and metadata | Tables, columns, keys, views, procedures, triggers and ownership clues | Application-level joins and undocumented conventions may not appear in DDL |
| API and message definitions | Endpoints, schemas, producers, consumers, authentication and versioning | Undocumented endpoints, transformations and runtime routing require additional evidence |
| Configuration and deployment artifacts | Connections, queues, schedules, hosts, containers, secrets references and environment mapping | Values can be environment-specific, templated or intentionally redacted |
| Documentation and interviews | Business meaning, exceptions, ownership, historical constraints and unwritten procedures | Record author, date and confidence; reconcile disagreements rather than silently selecting one account |
| Operational records | Observed traffic, job runs, failures, latency, data flows and production dependencies | Observations cover a time window and may miss infrequent paths |
Design a small, typed ontology
An ontology is the vocabulary and relationship model that makes queries consistent. Tailor it to the system and the decisions in scope. IBM’s modernization example uses typed nodes such as Class, Method, Table, BoundedContext, Technology, and DesignPattern, with relationships including OWNS_DATA, WRITES_TO, FOREIGN_KEY, DEPENDS_ON, INVOKES, and INTEGRATES_WITH. A 2025 paper describes customizable schemas for different programming languages and platforms, reinforcing that there is no universal ontology.
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Core node types
| Node type | Purpose | Examples of properties |
|---|---|---|
| Application | Portfolio-level system or deployable product | Criticality, owner, lifecycle, environment, version |
| BusinessCapability | Stable business ability supported by technology | Definition, value stream, accountable owner |
| Requirement and BusinessRule | Functional, regulatory or operational obligation | Text, priority, source, exception, validity |
| Module, Class and Method | Program structure at the granularity needed for analysis | Language, package, file, line range, symbol identifier |
| API and Integration | Callable or event-based interaction surface | Protocol, endpoint, schema, producer, consumer |
| Table and DataStore | Persistent data and its storage boundary | Schema, sensitivity, retention, steward |
| Technology and DeploymentAsset | Runtime, platform, host, container, queue, scheduler or managed service | Version, environment, region, lifecycle, dependency |
| BoundedContext | Candidate domain boundary with its own language and ownership | Scope, owner, invariants, status |
Relationship types and properties
Use explicit verbs rather than a generic “related to” edge. Distinguish a method that invokes an API from a class that merely depends on a library, and distinguish a table that is read from one that is written. Add relationship properties for source artifact, extraction method, observed time, valid time, confidence, review status, and reviewer. Stable identifiers should survive file moves and harmless renames where possible.
Keep evidence at the node or relationship level. A reviewer should be able to follow an assertion back to a code location, schema, configuration file, authoritative document, operational observation, or named expert. Preserve the original artifact separately from normalized graph facts so that extraction can be rerun and disputed facts can be audited.
Build the graph in seven controlled stages
1. Define scope and acceptance criteria
Select an application, business capability, or modernization decision for the first slice. Specify the source systems included, the time window for operational observations, the minimum confidence required for high-impact claims, and the questions the graph must answer. Decide how unknown, conflicting, stale and inferred facts will be represented before loading data.
2. Collect and preserve raw artifacts
Copy or reference source code, configuration, schemas, API definitions, integration maps, business rules, deployment artifacts and existing documents in a controlled location. Record repository commit, document revision, database snapshot, environment, collection time and access classification. Do not overwrite an earlier artifact when a new version arrives; retain lineage so a graph assertion can be tied to the version from which it was extracted.
3. Parse and normalize facts
Use language parsers and static analysis for program structure, database metadata for schemas, API tooling for endpoints and messages, and infrastructure or integration inventories for deployment relationships. Normalize names and identifiers while retaining the original spelling. Separate machine-extracted facts from manually entered and inferred facts. If two extractors disagree, store both observations and create a conflict for review rather than silently merging them.
4. Load nodes and relationships with provenance
Load the smallest useful ontology first. Every entity should have a stable identifier and a link to its source. Every important edge should carry evidence and confidence. A relationship inferred from naming conventions should not look identical to one observed in executable code or production traffic.
5. Validate high-impact claims
Ask application owners and domain experts to review business meanings, exceptions, shared data, undocumented flows, data stewardship and proposed boundaries. Compare graph results with source code, schemas, configuration and operational records. Track unresolved conflicts with an owner and due date. IBM’s worked workflow places architecture and domain review before downstream design and code generation; use the same checkpoint for any automated recommendation.
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6. Query for decisions and record the reasoning
Run targeted queries for dependency chains, incoming and outgoing calls, shared tables, integrations, orphaned artifacts, tightly coupled groups and candidate capability boundaries. Store a decision record containing the question, query or method, evidence, assumptions, confidence, risks, alternatives and reviewer. A graph result is an input to architecture work, not approval by itself.
7. Establish refresh and governance
Re-run extraction when repositories, schemas, integrations or deployments change. Record source versions and collection times, flag stale facts, and review conflicts. Apply access controls to source artifacts and sensitive graph properties. Define retention and deletion rules, especially for credentials references, personal data, regulated data and copied production metadata.
Useful graph queries for modernization
The exact syntax depends on the graph engine. The following patterns are intentionally illustrative; adapt labels and property names to your implementation.
Trace an application’s dependency surface
MATCH path = (a:Application {id: $application})-[*1..4]-(d)
RETURN pathLimit traversal depth and relationship types in production so that a common technology or shared host does not make every application appear directly dependent on every other one. Return source and confidence properties with each edge.
Find shared data ownership risks
MATCH (c1)-[:WRITES_TO|READS_FROM]->(t:Table)<-[:WRITES_TO|READS_FROM]-(c2)
WHERE id(c1) <> id(c2)
RETURN t, c1, c2Use the result to investigate transaction boundaries, stewardship, privacy classification, change coordination and whether the table is an integration contract rather than an internal detail.
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Group components around a business capability, then inspect cross-group calls, shared tables, rules that span groups, security obligations and operational coupling. A candidate with few code links can still be a poor boundary if it lacks data ownership or violates an invariant. Mark the result as a candidate until reviewed.
Find unsupported or orphaned artifacts
Look for methods, tables, APIs, queues and deployment assets with no incoming or outgoing evidence, then distinguish genuinely unused artifacts from extraction gaps. An orphan report is a triage list, not permission to delete production code.
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Use the graph to evaluate modernization options
Modernization choices should be compared across business outcomes, functional and technical fit, cost, readiness, risk, dependencies, data ownership, security and compliance, and feasible sequencing. AWS guidance treats modernization readiness as multidimensional rather than a single technical score. Add graph-specific evidence to each option.
| Option | Graph evidence to examine | Typical risk to test |
|---|---|---|
| Retain or remediate | Business criticality, defect hotspots, runtime support, owners, compliance exposure | Investment preserves an unmanageable dependency or unsupported platform |
| Rehost or replatform | Deployment assets, runtime dependencies, external connections, batch and recovery relationships | Infrastructure moves while data, coupling or operational constraints remain unresolved |
| Refactor in place | Module and method coupling, test coverage evidence, shared schemas, rule locations | Internal changes break undocumented consumers or duplicate business rules |
| Strangler or incremental decomposition | API seams, bounded-context candidates, data ownership, event flows and migration sequence | Temporary dual writes, inconsistent data or an integration path that cannot be isolated |
| Replace | Requirements, business rules, exception paths, data mappings, integrations and acceptance owners | Unrecorded behavior is omitted because the replacement matches only documented features |
For each option, attach benefits, risks, dependencies, assumptions and a proposed order. A decision is stronger when the graph shows not only what is connected, but also which evidence is missing and who must resolve it.
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What an example workflow shows—and what it does not
Aditya Sengupta’s IBM Developer tutorial describes an AI-assisted brownfield workflow that combines application artifacts, a Neo4j graph, an object store, reusable skills and human review. Its graph links business capabilities and requirements to code, APIs, database objects, integrations, deployments, documentation and organizational standards. The discovery example uses typed nodes such as Class, Method, Table, BoundedContext, Technology and DesignPattern.
The tutorial reports a Shopizer demonstration graph containing eight bounded contexts, 23 database tables, 18 Java classes, 26 methods and 12 external technologies. Those are counts from that specific demonstration, not a benchmark, target size or estimate for another estate. The page does not provide a publication year in the reviewed text, so the counts should not be presented as a year-attributed statistic.
The same example produces artifacts such as a graph evidence summary, service catalog, bounded-context map, architecture specifications, data-ownership matrix and migration roadmap. These are useful deliverable ideas, not mandatory documents. Its central principle is that the graph can serve as an authoritative source of architectural facts only when facts are traceable and reviewed.
A 2025 paper by Saravanan Krishnan, Amith Singhee, Keerthi Narayan Raghunath, Alex Mathai, Atul Kumar and David Wenk describes incremental analysis with a customizable knowledge graph. It groups dependent entities and repeats analysis to examine how a change in one group may affect others, reporting an initial study on two client applications. That supports incremental analysis as a technique; it is not a broad performance guarantee.
Choose tools against your constraints
No source establishes a universally best graph engine. Neo4j is the engine used in the IBM example, not a universal recommendation. Evaluate an engine or service against the work you actually need to perform.
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| Evaluation axis | Questions to answer |
|---|---|
| Language and artifact coverage | Can it parse the languages, database dialects, API formats, build systems and deployment artifacts in scope? |
| Typed modeling | Can nodes, relationships, constraints and evidence properties express the ontology without forcing everything into generic labels? |
| Traceability | Can a reviewer open the exact source artifact, version and location behind a fact? |
| Query and analysis | Can it traverse dependencies, detect shared data, compare subgraphs and support bounded-context analysis? |
| Incremental updates | Can changed repositories and schemas be reprocessed without rebuilding the entire estate, while preserving history? |
| Security and residency | Are access controls, encryption, audit logging, data residency and sensitive-property segregation adequate? |
| Workflow integration | Can it connect to repositories, architecture review, portfolio records, ticketing and deployment workflows? |
| Operations and scale | Who maintains extractors, schemas, pipelines, backups, indexes and query performance as the graph grows? |
| Portability and cost | Can data and provenance be exported, and what are licensing, infrastructure, support and migration costs? |
Common failure modes and recovery actions
The graph becomes a code catalog
Symptom: thousands of classes and methods are present, but no business capabilities, rules, owners or data meaning. Recovery: add a small set of business nodes and require every modernization question to connect technical evidence to a business decision.
Documentation is treated as truth
Symptom: diagrams and interviews override executable evidence without qualification. Recovery: retain both claims, label their sources and dates, and ask an owner to resolve the conflict. Programmatic extraction deserves priority, but it can also be incomplete.
Missing edges are interpreted as no dependency
Symptom: a component is declared safe to change because no relationship was loaded. Recovery: distinguish “not observed” from “verified absent,” record unavailable sources and test runtime or operational evidence where the impact is high.
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Symptom: a clustering algorithm becomes a service plan without review. Recovery: test candidate boundaries against business language, data ownership, invariants, security, operations, transaction behavior and migration sequencing; require named reviewers.
Provenance is added too late
Symptom: teams cannot explain why an edge exists or which version produced it. Recovery: make source, collection time, extraction method, confidence and review status mandatory for important facts before expanding scope.
The graph goes stale
Symptom: repository, schema or deployment changes are not reflected, so decisions rely on old dependencies. Recovery: trigger extraction from source changes, display freshness dates, flag stale high-impact facts and assign ownership for conflicts.
Sensitive information is copied without controls
Symptom: graph properties expose credentials references, personal data, regulated fields or production topology to users who do not need them. Recovery: classify data, minimize values, restrict access, audit use, encrypt storage and define retention and deletion policies. Microsoft Learn’s modernization guidance emphasizes inventory, quality, sensitivity classification, governance, security, validation and retention/deletion; apply those controls to graph artifacts as appropriate.
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- The initial scope and modernization questions are written down.
- Every application has an inventory record with owners, criticality, environment, dependencies and explicit unknowns.
- Source artifacts are preserved with versions, collection dates and access classifications.
- The ontology has typed nodes and relationship verbs appropriate to the questions in scope.
- Important nodes and edges carry provenance, confidence and review status.
- Automated, documented, operational and expert evidence can be compared without silently overwriting conflicts.
- Queries return dependency chains, shared data, integration impact and candidate boundaries with their evidence.
- Human reviewers validate business meaning, ownership, security, nonfunctional requirements and migration assumptions.
- Modernization options are compared on value, fit, cost, readiness, risk, dependencies and sequencing.
- Refresh triggers, stale-data handling, access controls, retention and deletion policies are operational.
When these conditions hold, the graph becomes a governed map of what the organization knows, what it only suspects, and what still needs investigation. That distinction is what makes it safe to use for legacy modernization decisions.
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