What Is RDF?

The Resource Description Framework (RDF) offers a uniquely flexible and powerful standard for describing web resources and data. Its inherent design allows for effective data integration from multiple sources by detaching data from its schema. This means you can apply multiple schemas, interlink them, query them as one, and even modify them without altering the original data instances. Built upon existing web standards like XML and URI (Uniform Resource Identifier), RDF ensures broad compatibility and accessibility.

The RDF Triples

At the very heart of RDF’s power lies the concept of “triples“. These are simple, three-part structures — subject, predicate, and object — that connect data pieces. Each triple expresses a single fact, relationship, or piece of data by linking different kinds of resources.

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 I programmed cell death> and 2. <apoptosis> type <biological process> .

Any data, regardless of its original format, can be converted into RDF data, with tools like Graph Automation facilitating this transformation.

The RDF Knowledge Graph

Knowledge graphs are richly interlinked, interoperable, and flexible information structures crucial for modern data management and AI applications. RDF is the foundational technology for building such graphs.

An RDF knowledge graph typically consists of the following components:

• Nodes: These represent resources, literals (data values – for example, the same as in XML), or blank nodes (anonymous resources). Resources are uniquely identified by URIs (for example, the well known URLs), allowing for global identification and disambiguation.
• Edges: These represent the facts or relationships between nodes and are called predicates.
• Context (Named Graphs): Collections of triples can be grouped into named graphs or contexts, enabling better management, provenance tracking, and security. Each edge can be seen as a quadruple: <subject, predicate, object, context>.

The following diagram demonstrates the expressivity of RDF where:

• Nodes are numbered for better readability and have URIs such as:

1. https://www.graphwise.com
2. https://www.linkedin.com/in/atanas-kiryakov
3. https://www.linkedin.com/in/andreas-blumauer
4. https://en.wikipedia.org/wiki/Sofia
5. https://en.wikipedia.org/wiki/Bulgaria

• Edges – for example, “partOf” or “knows”
• Context (Named Graphs) – for example, g1 and g2 used to manage components by provenance

Benefits of RDF Knowledge Graphs

RDF, when used for knowledge graphs, offers several significant benefits:

• 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-Star extension makes it easy to model provenance and other structured metadata.
• Formal Semantics: RDF comes with well-defined formal semantics, ensuring unambiguous interpretation of data, which is vital for automated reasoning and AI systems.
• 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: RDF is part of a broader ecosystem of W3C specifications (for example, SPARQL for querying, OWL for ontologies), ensuring seamless interoperability across diverse systems and applications.
• Standardization: As a W3C standard, RDF benefits from a robust, community-driven development process, ensuring its longevity and widespread adoption.

Attaching Properties to Edges: RDF-Star

One common criticism of traditional RDF was the difficulty in attaching metadata directly to a statement (an edge). For example, you may want to specify when a relationship was valid or who asserted it. RDF-Star addresses this by allowing you to attach properties directly to statements, making it possible to model richer contextual information.