Property Graph vs. RDF?

Executive Summary

Until now, customers have had to choose between RDF and Labeled Property Graphs (LPGs), accepting the tradeoffs and limitations of each. RDF offers robust semantics and established standards like Web Ontology Language (OWL) and SHACL, but it can be complex to manage. On the other hand, LPGs typically run faster for large-scale analytics but may lack the rich semantics that some enterprise scenarios require.

DataWalk changes the game. It is a no-compromise platform that provides the performance, scalability, and simplicity of property graphs, the modeling flexibility of RDF, and additional breakthrough capabilities such as enterprise prototyping, dynamic inferencing, and no-code analytics. Whether you’re integrating existing ontologies or building something new, DataWalk gives you the structure you need and the tools to work with it.

This paper provides architects and other technical experts with an in-depth review and comparison of RDF, LPGs, and the unique DataWalk approach.

Property Graph vs RDF

The table below provides a summary comparison of RDF graphs and labeled property graphs. 

DataWalk: The Best of Both Worlds

DataWalk eliminates the need for you to compromise with your graph technology selection. DataWalk gives you the performance and simplicity of a labeled property graph, the modeling flexibility typically associated with RDF, and adds additional capabilities:

• Dynamic inference through dependency refresh
• Enterprise prototyping without schema lock-in or rebuilds
• Integrated, no-code analytics for working directly with your graph

With DataWalk, you get a no-compromise platform that’s fast, flexible, and built to adapt.

Figure 1: DataWalk Provides The Best of LPGs and RDF.

RDF-like Flexibility with the Performance of LPG

In most LPG systems, you can attach properties to relationships, but you’re then locked into your initial modeling decisions. Changing the structure later often means updating the schema and rewriting parts of the graph.

RDF offers more flexibility but brings added complexity and performance overhead.

DataWalk solves this differently. With DataWalk’s Connecting Sets capability, you can evolve your model over time, without needing to rebuild it. The example below illustrates this. 

An Example with Comparisons

Step 1: Modeling Responsibilities Between Parties and Accounts

Let’s assume you need to model which parties (people or organizations) are responsible for which accounts, and what roles they play (Owner, Advisor, Controller, etc.), including when those responsibilities begin and end.

For this, you will want to model Parties, Accounts, and Responsibilities (with properties like role, startDate, endDate)

In LPG:
You can model this as a relationship with properties, which works until you need to connect that responsibility to something else, like a contract, policy, or data source. At that point, you’ll need to remodel it as a node with two relationships and refactor your queries.

Alternatively, you can start with a node representing the responsibility from the beginning, connected to both the party and the account. That adds an extra hop up front, but gives you more flexibility down the line.

Either way, you’re forced to make that structural decision early, before you know how the model will need to evolve.

In RDF:
You use multiple triples and supporting schema to capture role and time context. It’s expressive, but heavy to manage and slower at scale.

In DataWalk:
As shown in Figure 2, you use a Connecting Set (labeled “CS” below). This acts like a relationship for traversal but behaves like an object when needed. You can attach metadata and audit fields and link them to external references without changing the base model.

Figure 2: Connecting Set in DataWalk.

A direct comparison between an LPG and DataWalk is shown in Figure 3, below. In an LPG, you must choose between simplicity (a single direct connection at the cost of extensibility) or flexibility (adding intermediate nodes at the cost of additional hops). In DataWalk, this trade-off doesn't exist—you get simplicity and extensibility without compromise.

Figure 3: In LPG, you must choose between a connection without extendability or a set with two connections. DataWalk offers both, with one less hop as well.

Step 2: Modeling Transactions Between Accounts

Now let’s say your use case expands. You want to track financial transactions between accounts. You're modeling Transactions, with attributes like amount, timestamp, currency.

In LPG:
Once again, you must choose up front whether to model Transaction as a relationship (fast but inflexible) or as a node (flexible but more complex). If you guess wrong, you’ll need to restructure later.

In RDF:

You model each transaction using multiple triples and schema classes. You can link anything, but at a cost in volume, clarity, and performance. Figure 4 shows how RDF models can balloon when you add new components.

In DataWalk:
You create a Connecting Set: Transaction (Account → Account)
It starts simple—just a link between two accounts with properties. If you need to later link it to a fraud case or device, you can just extend it. No need to refactor the model.

Figure 4: RDF Model Expands with Addition of Transactions.

Step 3: Adding Fraud Investigations

Now, the model must support a new requirement: tracking fraud cases by grouping together potentially related transactions.

What you're modeling:

• A new object: FraudCase

• A new Connecting Set: Investigations (Transaction → FraudCase)

Figure 5: Fraud Investigations in DataWalk

As shown above and in the associated link chart in Figure 6, DataWalk allows you to connect one Connecting Set to another. This simplifies the analysis compared to the equivalent model in an LPG, which requires intermediate nodes.

Figure 6: In DataWalk, you can traverse from accounts to fraud investigations in just two hops. In a typical LPG model, you'd need three.

In LPG:
If you previously modeled Transaction as a relationship between accounts, you now need to remodel it as a node so it can be connected to a FraudCase. That means changing the schema, rewriting queries, and updating ingestion logic.

In RDF:
You can represent this by adding more triples and schema elements. It works—but as the model grows, so does its complexity and maintenance cost.

In DataWalk:
You simply add a new object: FraudCase, and a new Connecting Set: Investigations (Transaction → FraudCase). No existing structure needs to change. You don’t need to remodel or refactor anything. The graph just extends—cleanly and naturally.

DataWalk Case Study

To see how this works in practice, let’s walk through a case study where one of our customers used the model we introduced earlier—parties, accounts, responsibilities, transactions, and fraud cases. This is a financial institution that had previously invested in building out its ontology using RDF, OWL, and SHACL. While semantically rich, the previous structure had issues:

• Queries across linked triples were slow and required custom logic
• Graph analytics—like finding indirect relationships or pattern detection—weren’t feasible at scale
• Adapting the model to support new workflows, like fraud investigations, meant adding more layers of schema and triples

The Challenge

• RDF data with OWL and SHACL semantics already in place
• Preserve the existing structure and meaning
• Resolve performance issues in querying and analytics
• New use cases require grouping, filtering, and more
  

The DataWalk Approach

The customer adopted DataWalk and imported their existing ontology into DataWalk by ingesting RDF triples directly, including OWL and SHACL definitions. These were mapped onto DataWalk’s metamodel, preserving the original semantics without restructuring. From there:

• Account responsibilities were represented as a Connecting Set (Party → Account), retaining both role and timeframe context
• Transactions were represented as another Connecting Set (Account → Account)
• The existing ontology ended there. Fraud investigations had previously been handled in a separate system.

To support the new use case, the team extended the model within DataWalk, arriving at the model shown in Figure 5 above:

• Added a new object: FraudCase
• Created a third Connecting Set: Investigations (Transaction → FraudCase) to group relevant transactions into active cases

With DataWalk, there were no schema rewrites and no explosion of triples. The team just naturally extended the existing model.

Sample Analysis

One of the first questions analysts needed to answer was: Which account owners are involved in a given fraud case through high-value transactions?

This analysis was built entirely in the platform using the User Interface (UI); no code or query language was required. The step-by-step traversal for this analysis is shown in Figure 8. The result was a clear, traceable view of who was involved, how they were connected, and through which accounts and transactions. More importantly, this was all done on a single pane of glass without leaving the DataWalk platform, rewriting the model, or writing custom logic.

Figure 7: First Analysis on a New Model

Results With DataWalk

• RDF data was preserved and reused—no need to abandon existing modeling investments
• Fraud investigation support was added by simply extending the model, not rebuilding it
• Analysts explored layered questions without writing queries or switching tools
• The entire workflow—ontology mapping, enrichment, and analysis—happened in one place

Here, DataWalk’s advantage becomes clear. In most RDF or LPG systems, adding a new use case like fraud detection would require early planning or significant rework. DataWalk adapts to the questions, not the other way around. The organization didn’t need to start over. They simply brought their ontology into a platform that could work with it and grow with it.

Ontology Integration Workflow

The example above shows how an existing ontology can become extensible and actionable inside DataWalk. Now let’s look at how that process works more generally.

Turning an existing ontology into a working, analytics-ready graph in DataWalk is straightforward. The process is designed to work with your existing tools and evolve with your needs.

You can:

• Update the source ontology and re-import it into DataWalk
• Make changes directly in DataWalk, refining the metamodel or graph structure as your needs evolve
• Export updated models back out from DataWalk to share or sync with other systems

Whether you're adding new relationship types or reorganizing parts of the model, changes are easy to apply. You don’t need to start over. Most importantly, DataWalk and your source ontology system can update each other, enabling true two-way integration. That means users and architects stay in sync, and your knowledge graph remains a living, evolving asset.

Whether you’re reusing an existing RDF model or building something new, DataWalk gives you:

• A simpler alternative to heavyweight RDF stacks
• More modeling flexibility than traditional LPGs
• A platform built for dynamic inference, enterprise prototyping, and no-code analysis
• A single environment where your ontology becomes fully actionable, without compromise

More Than Just a Graph Model

Most graph technologies provide either a model or a query engine. DataWalk provides a full platform designed to work with data end-to-end, not just store or structure it.

Key capabilities include:

• No-code analysis – Analysts can filter, explore, and investigate graph data directly through the UI, without needing to write queries
• Built-in inference – Dependency refresh and calculated columns allow lightweight, automatic logic and rule propagation based only on what’s changed
• Enterprise prototyping – You can evolve your model as business needs change, without restarting, refactoring, or rewriting pipelines
• Integrated environment – Modeling, enrichment, and graph analytics all happen in the same space—no stitching together tools or exporting data between systems

These features make it easier to go from raw data to real insight, without needing separate platforms, custom scripting, or manual model rewrites.