What is RDF?

RDF is a standard for data interchange that is used for representing highly interconnected data. Each RDF statement is a three-part structure consisting of resources where every resource is identified by a URI. Representing data in RDF allows information to be easily identified, disambiguated and interconnected by AI systems.

What is RDF

RDF stands for Resource Description Framework and is a standard for describing web resources and data interchange, developed and standardized with the World Wide Web Consortium (W3C). While there are many conventional tools for dealing with data and more specifically for dealing with the relationships between data, RDF is the easiest, most powerful and expressive standard designed by now.

RDF Quick Facts
  • What is the Resource Description Framework (RDF)?

RDF is a general method of describing data by defining relationships between data objects.

  • Why is RDF a simple and flexible data model?

RDF enables effective data integration from multiple sources, detaching data from its schema. This allows multiple schemas to be applied, interlinked, queried as one and modified without changing the data instances.

  • What is RDF built around?

RDF is built around the existing Web standards: XML and URL (URI).

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The RDF Triples

The way RDF connects data pieces together is via triples (three positional statements).

In plain English, an RDF statement states facts, relationships and data by linking resources of different kinds. With the help of an RDF statement, just about anything can be expressed by a uniform structure, consisting of three linked data pieces.

Subject Predicate Object

Let’s consider the following statement: “Apoptosis is a type I programmed cell death”. Translated into RDF statements, the example fact (statement) would state the information in the following way: “Apoptosis is a type I programmed cell death” = > “Apoptosis of neutrophils” is the subject in two separate statements: 1. <apoptosis> is_a <type 1 programmed cell death> and 2. <apoptosis> type <biological process> .

Semantic Annotation

This is how the RDF model triples the power of any given data piece by giving it the means to enter endless relationships with other data pieces and become the building block of greater, more flexible and richly interconnected data structures.

It is important to know that all data, regardless of its format, can be converted to RDF data. (There are various tools for this – Ontotext’s tool is called Ontotext Refine and you can learn how to convert all kinds of data at: Working with Data Just Got Easier: Converting Tabular Data into RDF Within GraphDB).

The RDF Knowledge Graph

Being a powerful and expressive framework for representing data, RDF is used for building knowledge graphs – richly interlinked, interoperable and flexible information structures.

Leverage semantics to satisfy a conceptual definition of data and enable you to address emerging use cases. Use data models to inform the data without enforcing structure on it, meaning the widest scope of interpretation and the smallest bias toward implementation strategy. [1]

[1] Gartner, “How to Use Semantics to Drive the Business Value of Your Data”, Guido De Simoni, Last refreshed 2 April 2020, Published 27 November 2018

The following diagram demonstrates the expressivity of RDF.

RDF: Directed Labeled Cyclic Multigraph with Labels, Types, Logic and Semantics

The nodes in an RDF knowledge graph could be either resources, represented by a unique resource identifier (URI, e.g., the well known URLs), literals (e.g., the same as in XML) or auxiliary blank nodes. The types of the edges are called predicates, (e.g., partOf or knows). Named graphs or contexts (e.g., g1 and g2 above) can be used to manage components in the graph, (e.g., by provenance). Each edge in the graph represents a fact and can be seen as a quadruple <subject, predicate, object, context>.

Classes, predicates and named graphs are all defined as URIs. This way they can appear as nodes in the graph, get their descriptions, i.e. instance data and schema can be managed and accessed in an uniform model. The nodes in the above diagram are numbered for better readability; those must have URIs like the ones listed below:

  1. http://www.ontotext.com
  2. https://www.linkedin.com/in/atanas-kiryakov
  3. https://www.linkedin.com/in/vassil-momtchev
  4. https://en.wikipedia.org/wiki/Sofia
  5. https://en.wikipedia.org/wiki/Bulgaria

Knowledge graphs, represented in RDF, provide the best framework for data integration, unification, linking and reuse, because they combine:

  • Expressivity: The standards in the Semantic Web stack – RDF(S) and OWL – allow for a fluent representation of various types of data and content: data schema, taxonomies and vocabularies, all sorts of metadata, reference and master data. The RDF* extension makes it easy to model provenance and other structured metadata.
  • Formal semantics: All standards in the Semantic Web stack come with well specified semantics, which allow humans and computers to interpret schema, ontologies and data in unambiguous manner.
  • Performance: All the specifications have been thought out, and proven in practice, to allow for efficient management of graphs of  billions of facts and properties.
  • Interoperability: There is a range of specifications for data serialization, access (SPARQL Protocol for end-points), management (SPARQL Graph Store) and federation. The use of globally unique identifiers facilitates data integration and publishing.
  • Standardization: All the above is standardized through the W3C community process, to make sure that the requirements of different actors are satisfied – all the way from logicians to enterprise data management professionals and system operations teams.

Often RDF is criticized because it doesn’t allow for descriptions or properties to be attached to the edges in the graph and this is perceived as a disadvantage compared to Property Graphs. 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.

Want to learn more about RDF and RDF triplestores such as Ontotext’s GraphDB?

White Paper: The Truth About Triplestores
The Top 8 Things You Need to Know When Considering a Triplestore

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