Graph Database Free Download - Ontotext Graph DB ™

Build 360° Data View!

Establish Master Data as Linked Data.

Leverage Legacy Data as Knowledge Graph.

GraphDB 8 Release

Serves Enterprise Linked Data

Hundreds of billions of facts building the World Knowledge Graph are available in
the Linked Open Data cloud. Even more are gathered as Proprietary Data Sets.
Make them work for you!

Graph DB ™ is a semantic graph database that serves organizations to store, organize and manage content in the form of semantically enriched smart data.

A non-relational (NoSQL) database, Graph DB handles massive loads, queries and inferencing in real time. This allows for seamless integration of disparate data silos and a holistic 360-degree view of information.

In essence, Graph DB is a “semantic repository”, a database system used for storage, querying, and management of structured data. It uses ontologies to automatically reason about data.

Smart data management means better information products and services at a faster speed. This is achieved by handling information in a way that allows data items to be seamlessly mixed, exposed and shared across different platforms. As a result their relationships mapped with a high level of detail.

With Graph DB your data is stored in atomic facts that are easy to classify, reuse, combine share and integrate. Something more, such a mechanism for storing and managing data allows the creation of new facts that are implied in data.

How Graph DB Enables Smart Data Management?

Data integration and interlinking

The ability to recognize entities across multiple sources holds great promise helping to manage your data more effectively and pinpointing connections in your data that may be masked by slightly different entity references.

Merging information from various sources produces more accurate results, a clearer picture of how entities are related to one another and the ability to improve the speed with which your organization operates.

Graph DB works with globally unique identifiers, the most popular variant of which are the widely used URLs. All data elements (objects, entities, concepts, relationships, attributes) are identified with URIs, allowing data from different sources to merge without collisions. By doing this, you can align the same real-world entity used in different data sources. The most standard and powerful predicate used to establish mappings between multiple URIs of a single object is owl:sameAs.

Data integration and interlinking allows very easily merge of information from multiple sources including Linked Open Data or proprietary sources.

Single interconnected information space formed by structured data and text documents.

Materialised connections between free flowing, unstructured text, and data facts stored as database entities are extremely valuable. Such connections link entities from the database to the documents that mention them denoting relationships from which they were extracted.

Having these interconnections mapped, organizations can keep all of their data synchronized. The interconnected information space they form can be accessed through hybrid queries, combining the richness of the full-text search with the selectivity and precision of the databases.

Linked Open Data Compatibility

Hundreds of billions of facts building the World knowledge are available for free in the Linked Open Data cloud. Linked Data is an approach for publishing and interlinking data on the web to improve data interchange. The amount of openly released dataset grows exponentially in the recent years, covering everything from music, places, subjects of interest and products, to highly specific domains as drugs and bibliography.

When applied correctly, semantic facts can enhance your knowledge management and data discovery applications. You can answer more queries much faster and the results of the queries are highly relevant to your search.

W3C standards compliance

For graph databases such as Resource Description Framework (RDF) and SPARQL query language. Unlike other proprietary NoSQL specifications, these standards are solid and have as much industry support as the basic specifications that make WWW work: HTTP and HTML. Graph DB stores semantic facts in the form of subject – predicate – object using the Resource Description Framework. RDF is a standard model for data publishing and interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ. RDFS and OWL are its schema languages; SPARQL is the query language, similar to SQL. This ecosystem of standards is defined by the W3C consortium within community processes that involve all major data management vendors (IBM, ORACLE, HP, Microsoft, to name just few.

Expressive, rich and flexible data model

Graph databases are often referred as schema-less, where there is no schema that defines how database is organized. Graph DB supports all forms of metadata classification of data, express as ontologies, where ontologies are equated but limited to thesauri, taxonomic hierarchies of classes, class definitions and relations.


Graph DB can infer new knowledge from existing facts. This is called inferencing. Why is this important? You can create new facts from existing facts. Your queries run faster. Your results are more accurate. Not all graph databases support this capability and some apply different techniques to infer new semantic facts. The applications of inferencing span industries and use cases. Knowing that two people are connected through a series of other factual relationships can be helpful in identifying networks for a variety of purposes – for example social networks, fraud networks or terrorist networks. In physician referrals and clinical trials research, the ability to infer a doctor’s specialty based on the drugs prescribed can help you find doctors that you require. When analyzing economic markets, the ability to infer trading price points for commodities using weather and regional data may provide you with a competitive advantage.

Data Provenance

Tracking where data came from has so many important use cases. Users not only know the semantic facts but they know data sources, dates, levels of trust and other Metadata about the data.

Graph DB maintains a high level of detail, tracking where every piece of data came from.

Business Critical Applications of Graph DB

Probably the highest profile application of semantic technology to date, BBC’s 2010 World Cup web site is delivered using the OWLIM Enterprise semantic repository. A famous “call to action” by John O’Donovan (Chief Technical Architect, BBC) has spurred a flurry of semantic technology activity in world-known media companies, particularly in the UK and US.


KT Corporation, is the leading South Korean integrated telecommunication service provider. KT has an information and communications business that has the largest market share of the South Korean local telephone and high-speed Internet business. Megapass is a broadband telecommunication service offered by KT, which is using Graph DB as database.

How to Try Ontotext Products?

On Premise

In the Cloud

GraphDB Free Edition icon

Graph DB Free

Fully featured semantic graph database freely available for download. Offering a limited amount of processing capabilities.



Database as a service solution for small or medium database size and query load. No investment in software licenses, provisioning or maintaining a server.

Ontotext offers five editions of Graph DB™:

Standard, Enterprise, Database-as-a-Service on S4Cloud on AWS and Free.

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