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Choose the platform only after you define the modernization decisions the graph must support and the constraints of your legacy estate. Compare virtual, materialized and hybrid architectures; test the graph model, mappings, queries, reasoning, integrations, deployment and operations on representative data; then select the finalist that meets measured thresholds. Stardog, Amazon Neptune, Neo4j AuraDB and Ontotext GraphDB are all plausible candidates for different workloads, but the available evidence does not establish a universal winner.
Start with the modernization decision, not the product name
A knowledge graph is useful in legacy modernization when relationships are the difficult part of the problem. Source code, transaction definitions, database tables, interfaces, business capabilities, data assets and documentation are usually spread across repositories and teams. A graph can connect those artifacts so an architect can trace dependencies, identify a logical modernization boundary and inspect the likely impact of a change.
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A May 2025 paper, Incremental Analysis of Legacy Applications Using Knowledge Graphs for Application Modernization, describes a customizable graph built from source code and data schemas. Its dependency views are intended to help define modernization boundaries and inspect change impact. The initial study covered two client applications, so it demonstrates a method rather than proving production results across industries. Read the paper at arXiv.
Write down the decisions the graph must improve before you evaluate platforms. Typical examples include:
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- Which programs, jobs, tables and interfaces depend on a business capability?
- What is the inbound and outbound impact of replacing a service or database?
- Which data assets flow from an on-premises source into a cloud workload?
- Which applications share an entity, definition or policy?
- Which modernization boundary minimizes risk while preserving required behavior?
These questions determine whether you need ontology-based inference, fast traversal, federated access to live systems, a durable repository for extracted facts, graph exploration, entity resolution or graph-grounded retrieval. A platform selected without these questions tends to become an expensive inventory rather than a modernization instrument.
Choose the architecture pattern: virtual, materialized or hybrid
“Knowledge graph platform” can describe substantially different designs. AWS Prescriptive Guidance presents an architecture in which an ontology-based data-access layer maps SPARQL queries into SQL over relational sources using R2RML mappings and lightweight OWL ontologies. Ontop provides the virtual view, while Amazon Neptune stores knowledge that should be materialized. The design can query Neptune, relational systems, warehouses and data lakes independently or through federated queries. See AWS Knowledge Layer guidance.
| Pattern | What is stored where | Best fit | Trade-offs to test |
|---|---|---|---|
| Virtual | Facts remain in relational or tabular systems; mappings expose them as an ontology-defined graph at query time. | Freshness-sensitive data, strict ownership of source systems, and cases where duplication is undesirable. | Source load, query latency, join complexity, mapping maintenance, source outages and behavior after schema changes. |
| Materialized | Selected entities, relationships, inferred facts, semantic metadata or document-extracted knowledge are copied into a graph store. | Repeated graph traversal, durable reasoning, cross-domain relationships, document extraction and workloads that need graph-native performance. | Ingest cost, staleness, reconciliation, storage, update pipelines, lineage and the process for correcting copied facts. |
| Hybrid | Stable or high-value relationships are persisted while other data is queried virtually from operational stores, warehouses or lakes. | Large estates with mixed freshness, scale and ownership requirements. | Consistency across paths, federated query plans, failure handling, security propagation and operational complexity. |
Keep data in its existing system when virtual access meets freshness, latency and load requirements. Materialize only what benefits from persistence, traversal, inference or extraction. A hybrid design is often practical, but it must be treated as an explicit operating model rather than an automatic compromise. Validate connectors, query plans, source-system load, consistency and failure behavior in your environment.
When lineage is the primary use case
An AWS Database Blog example shows scanners inspecting on-premises and cloud sources, collecting metadata and ingesting assets and relationships into Neptune. The resulting catalog supports discovery, lineage, profiling, a business glossary, stakeholder and policy management, and AI-model documentation. The datasets are modeled as a graph connecting assets and relationships and published in the catalog. This makes Neptune a logical candidate when hybrid lineage and cataloging are central; it does not demonstrate that Neptune is the best platform for application-code modernization. Read the example at AWS Database Blog.
Build one requirements matrix for every candidate
Use the same matrix for each platform. For every requirement, record the evidence, a pass/fail result or measured value, assumptions, and the operational owner. Do not infer a capability from a marketing category.
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| Selection axis | Questions to answer | Evidence to collect |
|---|---|---|
| Modernization use case | Is the target code comprehension, dependency and change-impact analysis, lineage, cross-domain integration, transition planning or a downstream application? | Named decisions, user roles, acceptance scenarios and the artifacts that must be connected. |
| Data acquisition and movement | Can the platform connect to the actual languages, repositories, databases, catalogs and external systems? Must data be replicated, materialized, virtualized or combined? | Connector behavior, mapping effort, freshness, latency, consistency and source-system load. |
| Graph model and semantics | Do you need RDF and SPARQL, ontology and reasoning, property-graph traversal, or more than one representation? How will mappings and schema evolution be governed? | Modeling exercises, inference rules, migration of schema changes and explainability of derived facts. |
| Query and developer interfaces | Which query patterns, APIs, language drivers, exploration tools, BI integrations and catalog interfaces are required? | Working queries, API tests, authorization behavior, query governance and developer workflow. |
| Modernization workflow | Can the graph represent programs, transactions, tables, interfaces, dependencies and business boundaries? Can it be updated incrementally? | Impact-analysis tasks, incremental ingestion, version handling and architect review time. |
| Deployment and control | Does the design meet cloud, on-premises or hybrid placement, regional, tenancy, identity, audit and data-residency requirements? | Current product documentation and contract terms for security controls, isolation, backup and recovery. |
| Performance and scale | What graph size, traversal depth, reasoning workload, ingest rate, update rate, concurrency and latency targets apply? | Measurements on representative data; vendor figures remain claims until reproduced for your workload. |
| Availability and operations | What availability, replication, upgrade, monitoring, backup, patching and recovery objectives apply? | Runbooks, staffing model, recovery tests and day-two ownership. |
| Migration and support | What migration assistance, training, support response, ecosystem and export options are available? | Current service commitments, references, exit tests and acceptance criteria. A vendor case study is not an independent service-quality assessment. |
| Cost and lock-in | What are the license or consumption costs, infrastructure, migration, integration, operations and skills costs? | Current quotes and your own staffing and infrastructure assumptions. No neutral, current comparative total-cost study is established here. |
Match the data model and reasoning approach to the workload
RDF, SPARQL and ontology reasoning
RDF and SPARQL are appropriate when multiple teams need a shared semantic model, explicit mappings and inference across domains. Ontologies can define entities and relationships independently of the physical schemas, while reasoning can derive relationships that are not stored directly. This approach adds modeling and governance work: teams must manage ontology versions, mapping changes, inference costs and the explanation of derived facts.
Property-graph traversal and exploration
Traversal-oriented workloads emphasize navigating neighborhoods, paths and dependencies quickly. They can suit impact analysis, connected-asset discovery and interactive exploration, especially when users need to follow relationships rather than issue ontology-level semantic queries. Test traversal depth, branching factor, concurrent exploration and the ergonomics of the platform’s visual and API tools.
Entity resolution and graph-grounded retrieval
Some modernization programs need to reconcile records that refer to the same customer, system, service or data asset, or to ground retrieval applications in connected facts. These workloads require assessment of matching quality, provenance, embedding or search integration, access control and update behavior. Do not assume that a platform supporting a graph also provides the entity-resolution, search or retrieval workflow your project needs.
What the named platforms document
The following candidates have documented relevance to parts of this problem. The descriptions are not a head-to-head benchmark, and commercial terms, versions and service commitments must be verified during procurement.
| Platform | Documented fit | Important qualification |
|---|---|---|
| Stardog | Enterprise knowledge-graph capabilities, data virtualization, multi-model support and modernization while retaining existing systems. AWS Marketplace describes deployment through an AMI or CloudFormation template. | The listing says customers reduce data-preparation timelines by up to 90%. That is a Stardog claim in the marketplace listing, not an independently validated benchmark. Request methodology and applicability. |
| Amazon Neptune | Materialized graph component in AWS’s semantic-layer guidance; AWS also documents graph assets and relationships for lineage and catalog scenarios. | Strong logical fit for AWS-centered estates and lineage or semantic-layer workloads, but the examples do not constitute a neutral endorsement or prove suitability for every code-modernization case. |
| Neo4j AuraDB | AWS’s November 26, 2024 reference architecture describes AWS sources feeding a knowledge graph in Neo4j AuraDB, a SaaS offering running on AWS. It includes Neo4j Bloom for exploration and Graph Data Science for entity resolution, community detection and graph embeddings in a GraphRAG application. | The architecture demonstrates one connected-data and retrieval design. It does not establish the best choice for all modernization estates or workloads. |
| Ontotext GraphDB | GraphDB 10.8 documentation describes an RDF database with SPARQL, semantic inference, clusters, search integration and cloud deployment on AWS and Azure. | The documentation is version-specific. It says the Standard Edition is no longer offered but still receives legacy support; check the current version and commercial terms. |
Stardog
The AWS Marketplace listing positions Stardog for modernization that retains existing systems and combines virtualization with multi-model data. It describes deployment using an AMI or CloudFormation template. Treat the “up to 90%” reduction in data-preparation timelines as a vendor claim attached to that listing, not as a general industry statistic. During evaluation, verify the current release, licensing metric, deployment architecture, security controls, support terms and the exact workloads for which the claim is relevant. See Stardog Enterprise Knowledge Graph Platform on AWS Marketplace.
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Amazon Neptune
Neptune appears in two complementary AWS descriptions. In the semantic-layer design, it is the persistent store for selected graph facts, inferred relationships, cross-domain associations, semantic metadata and knowledge extracted from unstructured documents. In the Informatica example, it stores catalog assets and lineage relationships gathered from cloud and on-premises sources. Consider it when AWS integration, managed graph persistence, lineage or a semantic layer is central, then test code and schema ingestion rather than extrapolating from catalog examples.
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AWS’s reference architecture states: “This reference architecture demonstrates how AWS services and Neo4j can be used to create knowledge graphs.” Published November 26, 2024, it shows AWS data sources feeding Neo4j AuraDB, with Bloom for exploration and Graph Data Science for entity resolution, community detection and graph embeddings that ground a GraphRAG application. Use this evidence when connected-data discovery, enrichment or graph-grounded retrieval is part of the target architecture. Read the architecture at AWS’s Knowledge Graphs and GraphRAG with AWS and Neo4j.
Ontotext GraphDB
Ontotext’s GraphDB 10.8 documentation lists RDF, SPARQL, semantic inference, clustering, search integration and cloud deployment on AWS and Azure. It identifies Free and Enterprise editions and says Standard Edition is no longer offered while receiving legacy support. Because those details are tied to version 10.8 documentation, confirm the edition names, support status, deployment options and licensing before making a shortlist. See GraphDB 10.8 documentation.
An Ontotext-published case study about a scientific, technical and medical publisher lists requirements that are useful as acceptance-test prompts: efficient RDF import, semantic modeling and integration, full SPARQL, a Solr connector, multi-data-center availability and replication, cluster stability, daily updates, quarterly source releases, deployment automation, low administration effort and developer support. The case study says GraphDB was selected and that a migration service helped with transition, performance testing, system setup and acceptance testing. It is a vendor case study, not independent evidence of service quality or a guarantee for your estate. Read it at Ontotext’s publisher case study.
Run a proof of concept that can decide the purchase
A proof of concept should test modernization outcomes, not merely demonstrate that triples or nodes can be loaded. Use the same bounded domain and acceptance thresholds for every finalist.
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- Select a bounded modernization domain. Choose one service, transaction family, business capability or database boundary with known source code and schema artifacts. Include enough cross-system relationships to expose the real dependency problem. The legacy-application paper describes incremental analysis around logical boundaries and relationships among programs, transactions and database tables.
- Inventory the evidence. Collect source languages and repositories, programs, jobs, schemas, data-catalog metadata, documentation, change history and relevant external systems. Classify sensitive data, ownership and access controls before loading or virtualizing anything.
- Define decision queries. Write the exact questions the team must answer, such as what depends on this table?, which transactions touch this program?, what is downstream of this data asset? and what changes if this interface is replaced? Specify expected results and acceptable latency.
- Model the same scope on each finalist. Record ontology or property-graph design effort, mappings, identifiers, provenance, inference rules, schema evolution and the time required for an architect to validate the model.
- Test virtual, materialized and hybrid paths. Measure freshness, ingest duration, incremental-update behavior, cross-source joins, source-system load, query plans, stale-data windows and failure recovery. AWS’s semantic-layer guidance supplies an example of virtual and materialized facts being combined; your tests must establish whether that pattern works with your systems.
- Measure modernization tasks. Have representative architects perform dependency tracing, change-impact analysis, boundary definition and review of inferred relationships. Record correctness, explainability, latency, user effort and the percentage of artifacts successfully connected.
- Test operations and resilience. Exercise backup and restore, replication, upgrades, monitoring, access changes, connector failures, source outages and recovery objectives. Verify who owns each runbook and how incidents are escalated.
- Test developer and user interfaces. Run required SPARQL or traversal queries, APIs, language drivers, exploration views, catalog integrations and downstream applications. Include query governance and authorization tests, not just successful reads.
- Build a complete cost model. Use current quotes for software or consumption, infrastructure, storage, network transfer, migration, integration, support, operations and training. Add the internal skills needed to curate ontologies, mappings and pipelines. Do not convert a vendor productivity claim into a forecast without evidence from your workload.
- Set production gates. Require security and data-governance approval, an operating owner, documented rollback and export procedures, a migration plan, support commitments, recovery evidence and architecture sign-off before selecting a platform.
Account for governance, security and day-two work
A graph can make sensitive relationships easier to discover, so authorization must cover both the source facts and the relationships derived from them. Define which users may see code structure, data lineage, business definitions, inferred links and provenance. Test whether source permissions are preserved when data is virtualized, copied or federated.
Establish ownership for ontology terms, mappings, identifiers, inference rules and quality exceptions. Every materialized fact should have provenance, extraction time and a correction path. For virtual data, document source-system availability, mapping versions and the behavior users see when a source is unavailable.
Plan for schema and code evolution. A modernization graph should support incremental updates rather than requiring a full rebuild after every deployment. Keep versioned mappings and model changes, and retain enough history to explain why an impact result changed.
Deployment constraints belong in the shortlist from the start: cloud, on-premises or hybrid placement; regional residency; tenancy; identity integration; auditability; backup; recovery; patching; monitoring; and upgrade windows. The cited platform descriptions do not provide a complete, neutral comparison of these controls, so obtain current documentation and contract language.
Use an explicit decision rule
After the proof of concept, score each finalist against weighted requirements rather than choosing by feature count. A modernization program might assign the highest weights to correctness of dependency analysis, source integration, freshness, explainability and operational ownership; a lineage program might weight scanners, catalog integration, replication and governance more heavily; a GraphRAG program might prioritize retrieval grounding, entity resolution, embeddings and exploration.
- Reject any candidate that fails a mandatory security, residency, recovery or integration requirement.
- Prefer measured results over brochure capabilities: query latency, update lag, mapping effort, source load and recovery time should come from your POC.
- Separate architecture fit from product fit. A platform may be capable while the virtual/materialized pattern, ontology practice or operating model is wrong for your estate.
- Document uncertainty. Mark claims that depend on a vendor demonstration, version-specific documentation or an untested connector, and assign an owner and due date for validation.
- Protect the exit path. Test export of graph data, mappings, ontology definitions, provenance and application queries before production commitment.
Common selection mistakes
Choosing a database before defining the graph questions
A graph loaded with every available artifact can become a difficult-to-govern catalog. Start with decisions and queries, then collect only the data needed to answer them and expand the scope when value is demonstrated.
Confusing a graph database with a complete knowledge-graph architecture
The architecture also includes ontology design, mappings, ingestion, reasoning, integration, governance, access control and downstream use. A database alone does not provide those practices.
Assuming virtualization is free
Virtual access avoids some duplication, but mapping maintenance, source load, federated planning and outage behavior can be significant. Measure them under realistic concurrency and change conditions.
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The AWS/Neo4j design and the GraphDB publisher case document specific scenarios. They do not establish general modernization outcomes. Likewise, the Stardog “up to 90%” statement is a vendor claim tied to its marketplace listing.
Ignoring operations until after the model is built
Availability, replication, updates, backups, monitoring, patching and staffing can determine feasibility. Include them in the first evaluation cycle, especially when the graph becomes a dependency for impact decisions or production applications.
Final recommendation
Shortlist platforms by matching the modernization decision, data-movement pattern and semantic requirements to measured evidence. Use a virtual graph when freshness and source ownership dominate, materialize facts when traversal, inference or extracted knowledge needs durable graph performance, and combine both when the workload genuinely requires it. Evaluate Stardog, Amazon Neptune, Neo4j AuraDB and Ontotext GraphDB as workload-specific candidates, then let a representative proof of concept—covering correctness, integration, operations, security, recovery and total cost—make the final choice. No neutral head-to-head evidence supports declaring one universally best platform.
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