QLever IDE
& Graph Visualization Tool

Explore, query and visualize your graph data in QLever with gdotv.

A Purpose-Built IDE for QLever Developers

QLever is a high-performance SPARQL engine developed at the University of Freiburg, designed specifically for querying large RDF knowledge graphs.

By connecting gdotv to a QLever endpoint, you can exploit a combination that is especially appealing in research and open data contexts, where datasets may contain billions of triples and you need to explore relationships across vast entity spaces.

QLever handles the heavy lifting of rapid query execution, while gdotv transforms the results into navigable, intuitive graph visualisations. gdotv also provides a host of features to make exploring your QLever graph data easier, including no-code graph data explorer, data schema view, syntax highlighting, SPARQL query guardrails and more.

For teams working with Linked Open Data at scale, QLever and gdotv together offer a strong foundation for both performance and visual insight.

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QLever

open source

Query languages supported by gdotv: SPARQL

Learn more about QLever

An Overview of QLever

QLever is optimized for full-text search combined with SPARQL, making it particularly well suited to knowledge graphs that include large volumes of textual data, such as Wikidata or DBpedia. Its architecture prioritizes read-heavy analytical workloads, making it ideal for public knowledge graph exploration and research applications.

As an open source project with active academic development, QLever is a compelling option for researchers, data scientists, and organizations working with large-scale Linked Open Data, particularly where query performance over massive datasets is a primary concern.

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What’s Possible with QLever + gdotv

Easy Installation & Setup

Effortlessly integrate gdotv with your QLever graph database with a simplified plug-and-play database connector. The gdotv database client provides straightforward database connection and configuration instructions to get you started in less than a minute.

This QLever IDE is available to download for Windows, macOS, and Linux with a one-click installer to get you started instantly.

Connecting to QLever with gdotv is as easy as 1-2-3!

Manage all your database connections and queries from one central console.

Centralized Connection Management

gdotv can be used to query and compare data from a huge range of graph database vendors – simultaneously! We don’t just work with QLever: gdotv data visualization tools fully integrate with various other graph technologies, including all Apache TinkerPop™ compatible graph databases and most LPG and RDF providers.

gdotv is fully compatible with query languages like SPARQL, Gremlin, Cypher, Google SQL, SQL:2023, and more. Using an intuitive UI, you can switch between your connections and queries seamlessly.

Customizable Data Visualization

You can create custom visualization rules for your QLever graph database with gdotv to determine the look and feel of your data.

The gdotv database client boasts easy-to-configure style options that you can use to customize vertices and edges based on their labels and property values.

You can choose any color, size, or background image and adjust other elements to match your preference.

A sample graph display in gdotv using loading custom image file URL from vertex properties

The Graph Data Explorer enables no-code path exploration directly against your QLever graph database in just a few clicks

No-Code Data Exploration with the gdotv Graph Data Explorer

Use the built-in gdotv Graph Data Explorer to navigate your QLever data without using queries.

The Graph Data Explorer allows you to define path patterns to query from your data, using filters on both your vertices and edges. Elements in the declared path can be filtering according to their label, direction, and property values, allowing you to perform complex path-pattern searches with no code.

Instantly Load & View Your Data Model

It’s always important to understand and verify your data schema. This is why gdotv provides an overview of your data structure with the data model view.

For QLever, there are two methods to construct the data model. The first is that gdotv is able to directly retrieve the model, if available, using the QLever Schema API. Otherwise, gdotv can construct the data model directly from your data itself. This is always just a click away, meaning that you always have a bird’s-eye view of your vertices, edges, and properties right at your fingertips.

Access your data model

Create, update or delete vertices, edges, and properties interactively

Modify Your QLever Graph Data Directly within gdotv

Want to adjust something in your graph? Take a hands-on approach and manage your QLever graph directly.

Using gdotv, you can directly create, modify, and delete the edges and vertices – as well as modify the properties associated with them – allowing you to make changes on the fly.

Take Advantage of Syntax Validation

Using gdotv language highlighting and syntax checking for SPARQL makes your code easier to understand and visually parse, warning you of any error that would prevent it from executing against your QLever instance.

Intuitive interface with syntax highlighting and validation

gdotv’s SPARQL query guardrails detect a typo by checking queries against the graph structure

Take Control of your Queries with the gdotv SPARQL Query Guardrails

Since RDF is fundamentally schema-optional, gdotv won’t enforce any schema against your graph. It will, however, check your SPARQL queries against the inferred schema, meaning it will automatically generate warnings if it detects potential ontology clashes.

This gives you additional insight into malformed queries, even when they execute normally against your RDF triplestore.

Visualization Optimized for RDF

gdotv offers graph visualization specially developed for RDF triplestores like QLever.

The nodes and edges are detected from your query results, and any literals will be interpreted as properties. To improve readability, gdotv has built in IRI shortening and prefix handling. You can customize your graph labels further using inferred schema from the graph, such as via RDFS statements.

gdotv automatically performs prefix handling and IRI shortening

gdotv’s built-in documentation for SPARQL, right at your fingertips

State-of-the-Art SPARQL Editor

The gdotv SPARQL code editor is the most powerful available – it offers data schema-accurate auto completion and embedded official documentation for SPARQL to give you all the information and suggestions you need to write your QLever queries faster and more effectively.

QLever + gdotv Blog Posts

November 12, 2025

RDF Support Is Now Available in G.V() [v3.41.99 Release Notes]

Amir Hosseini

Frequently Asked Questions (FAQs)

How can I get started with QLever and gdotv?

Getting started with QLever and gdotv is easy. It’s just one simple installation, available on Windows, macOS and Linux. No prerequisites, additional configuration or installation required. You can get started in seconds.

gdotv automatically detects your connection setting requirements based on your database hostname and prompts you for the required information for an easy onboarding experience.

Want a step-by-step walkthrough? Make sure to check out our demo, Get Started with QLever + gdotv on YouTube.
What's the advantage of using RDF over LPGs?

Graph databases typically fall into two models: Resource Description Framework (RDF) and Labeled Property Graph (LPG).

The RDF model is used by QLever and refers to a highly standardized form of data management. Information is stored as a series of directed “triple” statements connecting a subject, predicate and object together. In addition, various languages built on RDF (such as RDFS and OWL) allow the construction of sophisticated ontologies and semantic reasoning.

The LPG model covers a broad class of databases that use nodes, edges, properties and labels to represent graphs. They are optimised for real-time use and excel in applications like fraud detection, social network analysis and recommendation algorithms. There are a wide range of methods to enforce schema compliance, with flexible conventions and definitions.

RDF is a rigorous framework with well-established vocabularies. The highly developed semantic structure makes it a powerful tool for inferring relationships and new insights from data. The high level of standardization in RDF also makes it ideal for inter-compatibility, as data from disparate sources can be integrated with ease. This makes RDF ideal for applications in large knowledge graph construction and research.
How do the SPARQL query guardrails work?

A SPARQL query engine performs no semantic inference over the stencil you define; it simply evaluates patterns and returns the nodes and edges that match them. As a result, a query can be syntactically valid, executable by any compliant engine, and even return results, while still being semantically meaningless with respect to the underlying data model.

In gdotv, the SPARQL query guardrails surface this problem before execution and even while the query is being written, giving you immediate, context-aware guidance. The system can infer, for example, if a predicate implies a specific kind of subject or object from those provided by the query.

Read a detailed discussion from Amir here.
What graph data visualization use cases do gdotv customers implement most often?
Organizing data derived via powerful data visualization tools have several real-life applications. Graph visualization and analysis resources allow users to make informed decisions based on actual data.

Here are a few examples of the type of companies or clients that can leverage QLever along with the robust data visualization aspect offered by gdotv:

Data scientists and managers: Visual data analysis allows users to uncover and understand emerging patterns and relationships between data points, discover valuable insights to make informed decisions on ways to transform business processes.

Cybersecurity groups: Build security graphs to represent protected assets, infrastructure, code dependencies or networks and visualize the connected relationships throughout the organization.

Business intelligence analysts: Create customized reports and visualizations that can be easily executed for analysts to investigate and communicate on interaction between business data points.

Fraud detection: Detect fraud patterns by modeling relationships between transactions and financial actors, then interactively visualize graph data to uncover and present potentially suspicious behavior.
How does gdotv secure my data?

gdotv Desktop runs locally on your device – no deployment needed and no handing over your data to a third-party system. Everything happens within your organisation’s network, and we have no access to your data.

gdotv Web versions run within your own environment, providing the same security benefits.

It’s easy to install and configure too: our setup wizard gets you connected to your graph database in seconds.
Can I connect more than one database at a time?

Yes! Whether you’d like to access multiple QLever instances, or interact with other graph technologies at the same time, gdotv doesn’t limit you to a single database. You can establish as many connections as you like from within the intuitive UI and seamlessly switch between them.

Connecting is easy, and there’s no need to manage multiple vendor-specific user interfaces. With gdotv, query and compare results from all your RDF and labeled property graph databases under one umbrella.

gdotv: The QLever IDE

Turn your graph data into connected insights when you use gdotv to query, explore, and visualize your QLever graph database.

Sign up for a free trial today and discover how gdotv amps up your productivity as the perfect QLever IDE and graph visualization tool.

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QLever IDE& Graph Visualization Tool