What is an RDF Triplestore?

The RDF triplestore is a type of graph database that stores data as a network of objects and uses inference to uncover new information out of existing relations. It's flexible and dynamic nature allows linking diverse data, indexing it for semantic search and enriching it via text analysis to build big knowledge graphs.

The RDF triplestore is a type of graph database that stores semantic facts. RDF, which stands for Resource Description Framework, is a model for data publishing and interchange on the Web standardized by W3C.

Being a graph database, triplestores store data as a network of objects with materialized links between them. This makes RDF triplestores the preferred choice for managing highly interconnected data. Triplestores are more flexible and less costly than a relational database, for example.

The RDF database, often called a semantic graph database, is also capable of handling powerful semantic queries and of using inference for uncovering new information out of the existing relations.

New call-to-action


The RDF Triplestore from Within

In contrast to other types of graph databases, RDF triplestore engines support optional schema models, called ontologies. Ontologies allow for a formal description of the data. They specify both object classes and relationship properties, and their hierarchical order.

The data in an RDF triplestore is stored in three linked data pieces

which are called a triple, hence the name triplestores. The triples are also referred to as ‘statements’ or ‘RDF statements’.

The subject->predicate->object format is able to take any subject and connect it to any other object by using the predicate (verb) to show the type of relationship existing between the subject and the object.

For example, ‘Joe sells books’ can be stored as an RDF statement in a triplestore and it describes the relationship between the subject of the sentence, “Joe”, and the object, “books”. The predicate “sells” shows how the subject and the object are connected.

The core concept of the RDF triplestore format as well as in the Linked Data paradigm is the Universal Resources Identifier (URI). The URI is a single global identification system used in the Web – a kind of a unique ID.

Often RDF triplestores are criticized because they don’t allow for descriptions or properties to be attached to the edges in the graph (when a set of triples are joined together, they they form a natural graph, where the predicates are interpreted as edges, and the subjects and objects are the nodes). This is perceived by some as a disadvantage compared to Property Graphs. However, this concern has been addressed with RDF-Star (abbreviated RDF*), which allows one to make statements about other statements and this way to attach metadata to the edges in the graph.

Empowering Linked Data

RDF triplestore databases are successfully used for managing Linked Open Data datasets, such as DBPedia and GeoNames, which are published as RDFs and are interconnected. Linked Open Data allows for querying and answering federated queries much faster and for obtaining highly relevant search results.

RDF triplestores query diverse and evolving data from different sources, which is more cost-efficient and less time-consuming.

Since universal standards apply to RDF triplestores, they make moving data from one triplestore to another trivial.

Enterprise Deployments of an RDF Triplestore

RDF triplestores handle huge amounts of data, which improves the knowledge discovery and analytics capabilities of organizations. What’s more important is that triplestores are able to infer implicit facts out of the explicit statements. Inferencing relationships out of the original data, with the help of a semantic graph database such as Ontotext’s GraphDB, turns information into knowledge. This allows organizations to uncover hidden relationships across all their data.

Having gained more knowledge than competitors, enterprises can more easily scale up that knowledge into smarter solutions and have the upper hand in the competition. Big organizations in Media & Publishing, Healthcare and Life Sciences and the Financial Services sectors are already widely using RDF triplestores to manage unstructured and structured data.

Referencing Unstructured Data

Triplestores also facilitate many text analytics techniques such as extracting information from unstructured data and enriching content. By ‘learning’ the meaning as well as the context in which entities are used, machine learning algorithms can classify entities and disambiguate between them (for example, whether “Paris” in a text refers to “Paris, France”, or “Paris, Texas”, or “Paris Hilton”).

Apart from defining relationships, RDF triples also allow links between databases with structured data and documents that contain unstructured, free-flowing text. In this way, RDF triplestores connect entities from databases to documents that mention these entities. Ontotext’s RDF triplestore GraphDB also has a virtualization functionality that makes it possible to create a virtual graph by mapping the columns and rows of a table to entities in the graph. Thus, it becomes possible to retrieve information from external relational databases and have it play with the knowledge graph.

Other Use Cases

Graph databases, and RDF triplestores in particular, have immense benefits for organizations aiming to make use of context as well as content. Any solutions based on an RDF knowledge graph and combined with advanced text analytics techniques help organizations gain a competitive edge, create more value and tap into new sources of revenue.

Is an RDF triplestores like Ontotext’s GraphDB your answer?


New call-to-action


Ontotext Newsletter