Build the graph as governed, evidence-linked decision support—not as an automatic architecture generator. Start with the modernization questions and an application portfolio, collect code and operational evidence, define a small typed ontology, extract facts with provenance and confidence, validate important relationships with owners and domain experts, and then query the graph for dependencies, data ownership, bounded-context candidates, and migration risk.
A useful graph connects business capabilities, requirements, and rules to applications, modules, methods, APIs, tables, integrations, technologies, deployments, and responsible teams. Those connections let you answer questions that a code index cannot: which capability depends on a failing program, which systems write to a shared table, and what would be affected if an integration changed.
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What a modernization knowledge graph is
A modernization knowledge graph is a typed network of facts about a legacy estate. Nodes represent business and technical entities; relationships represent facts such as “writes to,” “invokes,” “owns,” “depends on,” or “integrates with.” Each important node and relationship should point back to evidence and carry a confidence or review state.
The graph is different from a dependency diagram or an inventory spreadsheet. A spreadsheet may list an application and its owner, while a graph can connect that application to a business capability, a Java class, a method, a database table, a message queue, an external system, a deployment asset, and the requirement that the code implements. The links make multi-step questions queryable.
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Do not treat a graph edge as proof merely because it exists, or treat a missing edge as proof that no dependency exists. A missing relationship can mean that a repository, configuration file, generated artifact, or operational record was unavailable, or that an extractor did not understand the format. Every decision should therefore retain its evidence, assumptions, and unresolved gaps.
Start with decisions, not with a graph database
Write down the questions the modernization program must answer before choosing a storage technology or scanning every file. AWS Prescriptive Guidance recommends starting with business drivers, stakeholder requirements, an initial portfolio inventory, and a prioritized assessment plan. Questions should be specific enough to produce an action.
Questions worth designing for
- Which business capability and requirements depend on this application or program?
- Which systems call this API, publish to this topic, or consume its messages?
- Which tables are read or written by multiple components, and who is accountable for the data?
- What is the likely blast radius of changing a shared integration, schema, or authentication service?
- Which parts of a monolith form plausible bounded-context candidates, and what evidence supports each boundary?
- Which modernization option offers the best business value and feasible sequence for a particular application?
Turn each question into a definition of done. For example, a “shared-table” answer might require two independent source locations, a schema record, a current owner, and a review date. A “bounded-context candidate” might require cohesive business terms, a data-ownership hypothesis, dependency counts, security considerations, and approval from the relevant domain experts.
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Choose an initial slice: one application, one business capability, or one modernization decision. Define the environments included, the time period represented, sensitive data that must be masked, and the people who can approve facts. An intentionally narrow first slice is easier to validate than an estate-wide graph populated with unreviewed guesses.
Record what the graph will not claim. It may identify a candidate service boundary, but it cannot by itself establish organizational ownership, regulatory acceptability, latency requirements, or a safe cutover plan. Those require business, security, operational, and architecture review.
Build a portfolio and evidence inventory
For every application and major infrastructure component, capture the facts needed to prioritize discovery. AWS guidance recommends progressive collection and reassessment rather than waiting for a perfect dataset.
| Inventory area | Examples of facts to capture | Why it matters |
|---|---|---|
| Identity and purpose | Name, primary function, business capability, product or version | Connects technical assets to outcomes and avoids orphaned analysis. |
| Criticality and risk | Business criticality, known issues, risks, recovery expectations | Helps sequence discovery and modernization work. |
| Runtime context | Environment, deployment location, operating platform, schedules | Separates production behavior from test or obsolete artifacts. |
| Dependencies | Databases, APIs, queues, files, external systems, shared services | Provides the first dependency edges and likely blast radius. |
| People and governance | Owners, stakeholders, support teams, compliance context | Identifies reviewers and constraints that code cannot reveal. |
| Evidence metadata | Source location, collection time, version, extraction method, confidence, gaps | Makes every assertion traceable and time-bounded. |
Rank sources instead of pretending they are equivalent
Use source provenance and a trust policy. AWS states that programmatic data is typically more trusted than written documentation, static data, and institutional knowledge; this is a prioritization principle, not a claim that automated extraction is infallible. A runtime trace can show that a call occurred, while source code can reveal a dormant path, and an owner interview can explain a business exception. Keep all three, label them, and resolve conflicts explicitly.
For each fact, store the artifact or interview reference, the collector, the observed time, and whether the value was machine-extracted, documented, inferred, or confirmed by a named reviewer. Mark unknowns as unknowns. Do not fill a blank owner, table purpose, or integration protocol with a plausible guess.
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Collect the artifacts behind each application
For an individual application, gather the widest practical set of evidence before modeling relationships. IBM Developer’s brownfield modernization tutorial uses source code, database schema, integrations, configuration, business rules, and documentation as discovery inputs.
Technical and operational inputs
- Source repositories, build files, dependency manifests, generated-code settings, and release branches.
- Configuration files, environment variables, feature flags, secrets references, scheduler definitions, and connection settings. Store sensitive values securely; the graph needs the relationship and identity, not an exposed credential.
- Database schemas, DDL, views, stored procedures, triggers, indexes, data dictionaries, and migration scripts.
- API specifications, interface-control documents, message schemas, queue or topic definitions, file layouts, and integration mappings.
- Operating-system jobs, deployment manifests, infrastructure definitions, container images, network rules, certificates, and monitoring configuration.
- Operational records such as call logs, batch schedules, incident notes, and service-level expectations when policy permits their use.
Business and organizational inputs
- Business capabilities, requirements, business rules, exception policies, and terminology.
- Application purpose, critical processes, data classifications, retention obligations, and compliance considerations.
- Owner and stakeholder lists, support responsibilities, known workarounds, and institutional knowledge.
Keep the original artifacts in a controlled repository separate from graph facts, and link each graph entity back to a precise file, schema object, document section, log period, or interview record. IBM’s example stores artifacts and discovery documents separately while producing a typed graph and human-readable discovery material.
Design a small, typed ontology
Begin with concepts required by the first decisions, then expand deliberately. There is no universal ontology for every language, platform, or organization. A 2025 paper by Saravanan Krishnan, Amith Singhee, Keerthi Narayan Raghunath, Alex Mathai, Atul Kumar, and David Wenk describes customizable schemas for different programming languages and platforms, supporting the case for a system-specific model.
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| Node type | Typical properties | Example relationships |
|---|---|---|
| Application | Name, purpose, criticality, environment, owner, version | IMPLEMENTS, DEPLOYED_AS, DEPENDS_ON |
| BusinessCapability | Definition, business owner, criticality | REALIZED_BY, CONSTRAINED_BY |
| Requirement or BusinessRule | Statement, source, validity, reviewer | IMPLEMENTED_BY, APPLIES_TO |
| Module, Class, or Method | Language, package, source location, signature | INVOKES, DEPENDS_ON, WRITES_TO, READS_FROM |
| API or Integration | Protocol, version, endpoint, message type | CALLS, PUBLISHES, CONSUMES, INTEGRATES_WITH |
| Table or DataStore | Schema, classification, owner, retention | FOREIGN_KEY, OWNED_BY, READS_FROM, WRITES_TO |
| Technology | Product, language, runtime, version, support state | USED_BY, DEPLOYED_ON |
| DeploymentAsset | Host, cluster, region, environment, release | RUNS, CONNECTS_TO |
| BoundedContext | Business meaning, candidate status, reviewer | CONTAINS, SHARES_DATA_WITH, DEPENDS_ON |
IBM’s worked example uses typed nodes including Class, Method, Table, BoundedContext, Technology, and DesignPattern. Its relationship vocabulary includes OWNS_DATA, WRITES_TO, FOREIGN_KEY, DEPENDS_ON, INVOKES, and INTEGRATES_WITH. Use such names as a starting vocabulary, not as a mandatory standard.
Properties that make facts auditable
Give every entity a stable identifier and retain its human-readable name. Add language or platform, source artifact, source location, extraction method, observed time, valid time where known, confidence, review status, and sensitivity classification. Put provenance on relationships as well as nodes: “component A writes table B” needs its own evidence and review state.
Separate observed facts from interpretations. “Method X calls endpoint Y” may be extracted from code. “These methods form a customer-profile boundary” is an architectural interpretation that should reference the supporting facts and remain marked as a candidate until reviewed.
Extract facts while preserving raw evidence
Automate repeatable discovery
Use parsers and static analysis for source code, database metadata for schemas, API specifications for interfaces, and infrastructure or integration inventories for deployment relationships. Prefer deterministic extraction for identifiers, signatures, imports, table references, endpoint paths, and declared configuration. Record extractor version and run time so a later change can be explained.
Normalize names without destroying originals. A table’s physical name, a business synonym, and a legacy abbreviation may all be needed. Keep the original spelling and add aliases or terminology mappings rather than replacing evidence with a cleaned label.
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Handle inference and manual facts explicitly
Some facts cannot be reliably extracted: a batch file may encode a business rule, a shared table may have an informal owner, and a service boundary may exist only in team practice. Add these facts through documented review or interviews, identify the contributor, and label them as manually supplied or inferred. If two sources disagree, preserve both claims with their provenance and create a conflict for resolution.
Store raw source artifacts and generated discovery documents outside the graph, with access controls and retention rules. The graph should contain enough pointers for an architect to reproduce an assertion without exposing credentials or unrestricted production data.
Validate the graph before using it to design services
Validation is where a useful graph becomes governed decision evidence. Ask application owners and domain experts to check business meanings, exceptions, shared data, undocumented flows, and ownership. Compare high-impact claims against code, schemas, configuration, and operational records.
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Use review gates for consequential claims
- Technical check: confirm that the cited source artifact actually supports the node or relationship and that the extractor interpreted the syntax correctly.
- Business check: confirm capability names, requirements, rules, exceptions, and the meaning of data fields with a domain representative.
- Ownership check: identify who can approve data ownership, API contracts, security controls, and operational responsibility.
- Architecture check: review candidate boundaries, cross-context dependencies, nonfunctional requirements, and migration assumptions.
- Decision record: capture the query or method, evidence, assumptions, confidence, risks, reviewers, and unresolved conflicts.
IBM’s workflow requires architects and domain experts to validate architecture specifications before downstream design and code generation. Follow the same principle even when your graph is built with different tools: graph-derived boundaries are candidates, not authorization to cut code or move data.
Define confidence without false precision
A simple status vocabulary is often more useful than an invented percentage: observed for directly evidenced facts, corroborated for independently supported facts, inferred for reasoned interpretations, reported for unverified statements, and disputed for conflicting claims. Add a review date and an owner for follow-up. Confidence should describe evidence quality, not the importance of the entity.
Query the graph for modernization decisions
Queries should produce decisions and evidence packages, not attractive diagrams. Most graph engines can express equivalent patterns; adapt the syntax to your chosen platform.
Dependency and blast-radius analysis
Find incoming callers and outgoing dependencies for an application, API, class, or table. A bounded traversal of one to three relationship hops is often easier to review than an unrestricted network dump. Return the path, relationship evidence, environment, and last-observed time. Group results by business capability and owner so a technical dependency becomes an actionable stakeholder list.
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Shared data and ownership
Ask which components read or write the same table, view, file, or event schema. Distinguish read-only consumers from writers, and distinguish a declared foreign key from an inferred join. A shared table is not automatically a bad design, but it is a migration dependency that requires ownership, consistency, security, and cutover analysis.
Candidate bounded contexts
Combine business vocabulary, capability links, data ownership, call patterns, and change history where available. A candidate group should have meaningful internal cohesion and explicitly listed external dependencies. Report evidence such as shared methods, tables, integrations, and rules; do not present a graph cluster as a finished domain model.
Modernization portfolio prioritization
Compare applications across business value, functional and technical fit, cost, readiness, security and compliance, coupling, data ownership, operational risk, and feasible sequencing. AWS guidance frames modernization readiness as multidimensional across business, functional, technical, and financial significance. Store the rationale and dependencies with the portfolio decision so a score does not become an unexplained ranking.
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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. The graph links business capabilities and requirements to code, APIs, database objects, integrations, deployments, documentation, and organizational standards.
Its Shopizer demonstration contains 8 bounded contexts, 23 database tables, 18 Java classes, 26 methods, and 12 external technologies. Those are counts from that particular tutorial example, not a benchmark, target size, or industry average. The reviewed page does not state a publication year for the example.
The tutorial uses graph queries to produce candidate service boundaries and describes deliverables such as a graph evidence summary, service catalog, bounded-context map, architecture specifications, data ownership matrix, and migration roadmap. These are useful examples, not mandatory documents for every program.
Use incremental analysis for changing legacy systems
A 2025 paper, “Incremental Analysis of Legacy Applications Using Knowledge Graphs for Application Modernization,” by Saravanan Krishnan, Amith Singhee, Keerthi Narayan Raghunath, Alex Mathai, Atul Kumar, and David Wenk, describes a customizable graph for incremental analysis. It presents logical grouping of dependent entities and repeated analysis to examine how a change in one group may affect others, with an initial study on two client applications.
Apply that idea operationally by storing source versions and extraction runs, identifying changed artifacts, and recomputing affected facts rather than rebuilding the entire estate blindly. Revalidate relationships whose evidence changed, and retain the previous observation so a reviewer can see whether a dependency was removed, renamed, or simply missed by a new extractor. The paper describes an approach, not a broad performance guarantee.
Keep the graph current and governed
Graph quality decays when repositories, schemas, integrations, and deployments change without corresponding updates. Treat population as an iterative discovery pipeline.
- Schedule extraction after source, schema, integration, and deployment changes, or trigger it from repository and release events.
- Record collection time, source revision, extractor version, and environment for every run.
- Flag stale facts after an agreed review interval and route high-impact conflicts to an owner.
- Version ontology changes so a renamed relationship does not look like a sudden architecture change.
- Control access to source artifacts and sensitive graph properties using least privilege.
- Classify data sensitivity, define retention and deletion policies, and remove obsolete copies safely.
- Measure coverage and review status, not just node count: percentage of critical applications with owners, percentage of high-risk relationships corroborated, and number of unresolved conflicts.
Microsoft Learn guidance on application modernization, updated 2025-05-19, emphasizes inventory, data quality, sensitivity classification, governance, security, validation, and retention or deletion policies for modernization data. Apply those controls to graph content and its linked artifacts as appropriate to your organization.
Compare implementation approaches on the dimensions that matter
No source establishes a universally best graph engine. Neo4j is the engine used in IBM’s tutorial, not a universal recommendation. Compare an implementation against the decisions it must support.
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| Evaluation axis | Questions to ask |
|---|---|
| Language and artifact coverage | Can it parse the languages, database dialects, API formats, infrastructure definitions, and message systems in scope? |
| Typed modeling | Can it represent the node, relationship, property, and provenance types your questions require? |
| Evidence traceability | Can every important fact link to a source artifact, location, version, collector, and review state? |
| Query and analysis | Can reviewers find dependency chains, shared data, change impact, clusters, and orphaned assets without exporting opaque results? |
| Incremental updates | Can changed repositories and schemas be reprocessed while preserving historical observations? |
| Security and residency | Are access controls, encryption, audit logs, masking, and data-location requirements satisfied? |
| Workflow integration | Can results flow into repositories, architecture reviews, issue tracking, and migration plans? |
| Skills and operations | Can your team operate the parsers, graph service, access model, backups, and ontology lifecycle? |
| Scale and cost | What volume, query concurrency, storage, licensing, support, and migration costs are realistic for your estate? |
| Portability | Can you export source-linked facts and mappings if the engine or vendor changes? |
Evaluate with a representative slice rather than a synthetic demo: include a database-heavy component, an integration, generated or legacy syntax, an undocumented rule, and at least one disputed ownership claim. Success means reviewers can trace and act on the answers, not merely that the graph contains many nodes.
Common failure modes and practical fixes
Scanning everything before defining a use case
Symptom: millions of low-value nodes and no decision anyone trusts. Fix: select a bounded question and a critical application slice, then add entities only when they support that question.
Treating documentation as current truth
Symptom: diagrams and runbooks disagree with production behavior. Fix: retain documentation but rank it against programmatic and operational evidence, record dates, and ask owners to resolve conflicts.
Assuming missing edges mean no dependency
Symptom: a migration breaks an undocumented batch, file exchange, or runtime-generated call. Fix: display discovery coverage and unknowns, add operational evidence where permitted, and mark extractor limitations.
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Symptom: a technically cohesive cluster becomes a service despite incompatible ownership, security, or transaction requirements. Fix: label boundaries as candidates and require business, data, security, and operational review.
Ignoring sensitive data in the graph
Symptom: source fragments, credentials, customer identifiers, or regulated metadata are copied into an overly broad system. Fix: classify fields, mask or tokenize values, separate raw artifacts, restrict access, and define retention and deletion rules.
Building a static one-time snapshot
Symptom: the graph is accurate at launch and misleading after the next release. Fix: version inputs, rerun extraction incrementally, flag stale facts, and assign owners for review.
A deliverable sequence that keeps work actionable
- Decision brief: modernization goals, questions, scope, stakeholders, constraints, and success criteria.
- Portfolio inventory: applications, environments, criticality, owners, dependencies, risks, compliance context, and known gaps.
- Evidence catalog: repositories, schemas, APIs, integrations, configurations, deployments, documents, operational records, collection times, and trust labels.
- Ontology and mapping guide: node types, relationship types, identifiers, terminology rules, provenance fields, confidence states, and sensitivity handling.
- Validated graph slice: source-linked entities and relationships for the first application or capability, with unresolved conflicts visible.
- Decision queries: dependency paths, shared data, candidate boundaries, orphaned assets, and modernization-readiness views.
- Review package: evidence summary, assumptions, owners, risks, security and compliance considerations, and an approved or rejected recommendation.
- Operating model: extraction triggers, ontology change control, access reviews, retention, deletion, quality measures, and responsibilities.
The result should let a reviewer move from a modernization decision to the exact code, schema, configuration, document, or expert statement that supports it. That traceability is the graph’s value: it turns an undocumented estate into a navigable body of evidence while keeping judgment, accountability, and uncertainty visible.
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