The Resource Description Format (RDF) is a data model that has been a W3C standard since 1999 and continues to be the core standard of semantic technology. By giving you a way to describe all facts as simple, three-part statements, RDF makes it easier to mix and match and connect different datasets.

These three-part statements are known as triples. You can think one as the equivalent of a single cell in a spreadsheet where the column name is the property name, the row name is the resource identifier (the thing described by the property) and the contents of the cell are the property value. The resources, properties, and sometimes values all have Web-wide unique identifiers known as URIs so that they can be linked to from anywhere, can be accessed using normal Web and Internet technology, and can be aggregated with other data without fear of name conflicts.

The SPARQL Protocol and RDF Query Language (SPARQL) is a set of standards for querying and updating RDF data as well as for transmitting queries and results over a network.

The query language is the most commonly used of these specifications and forms the basis of the SPIN rules and constraint language and the SPARQLMotion and SPARQL Web Pages scripting languages developed by TopQuadrant to make application development easier and more efficient. In the words of World Wide Web inventor Tim Berners-Lee, “Trying to use the Semantic Web without SPARQL is like trying to use a relational database without SQL.”

SPARQL is commonly used to perform data validation and, with capabilities such as transitive closure operators and property functions, perform query time reasoning. SPARQL can also be used to construct and persist new facts (triples) based on the existing facts. TopBraid distributions include a complete OWL RL profile implemented using SPARQL.

The RDF Schema (RDFS) language is a vocabulary description language that lets you describe classes and properties using RDF itself—that is, RDFS descriptions themselves are a series of three-part statements about classes and properties. Sharing of such descriptions helps to make RDF-based data and applications more interoperable. While RDFS stands for RDF Schema Language, it is not a traditional schema language. For example, it can’t support data validation because with RDFS, data is never invalid. It is designed to support certain, very basic type of inferencing.

Use of RDFS (and its more powerful cousins, OWL and SHACL) are optional when working with RDF.

SHACL is an official W3C standard, a modeling language for describing a set of conditions data should meet – specifically, data in the knowledge graphs. These conditions are defined in structures called SHACL shapes. Using SHACL you can describe what properties are required to have values, a number and type of allowed values and much more.

SHACL Rules support reasoning applications. Rules specify new facts that can be inferred from the existing facts.

SHACL provides an alternative to using RDFS and OWL for ontology modeling. It can also be used together with RDFS/OWL. In addition to supporting rules for specifying requirements (constraints) your data must meet, SHACL also offers a way to specify rules that infer new facts from the available data.

Yes, both TopBraid EDG and TopBraid Composer Maestro Edition can auto-convert RDFS/OWL to SHACL.

The Web Ontology Language (OWL) builds on RDFS to provide a vocabulary for making more sophisticated modeling statements. With the appropriate OWL-enabled tools, these descriptions may be used as the basis to infer new facts from combinations of other facts.

Note that because RDFS and OWL are based on the Open World Assumption, they are not intended to be used for data validation. Open World Assumption means that one can’t make conclusion based on the absence of facts. For example, while OWL allows us to say that a Person must have one and only place of birth, absence of the place of birth information, will not be considered inconsistent. Having multiple places of birth may not (depending on what else is in the model) be considered inconsistent either. SHACL, on the other hand, operate under the Closed World Assumption and is a better fit for typical data validation, data modeling and reasoning needs of many business applications.

Various profiles of the OWL specification have been published to more efficiently deal with certain classes of use cases; the original version of OWL had OWL Full, OWL-DL, and OWL Lite, and OWL 2 has OWL RL, OWL EL, and OWL QL.

Because data models are themselves expressed using RDF triples, they too can be easily aggregated. Knowing this, system developers often start small and grow a semantic technology system organically, adding pieces as their system scales up. Instead of forcing different stakeholders to agree on a common model that may not be ideal for either of them, the differences can themselves be modeled and made part of an application.

The main challenges of Big Data are usually defined as the ability to handle the greater volume, velocity, and variety of data becoming available. In a recent report titled “Big Data Adoption in 2013 Shows Substance Behind the Hype,” the Gartner technology research and advisory company wrote “Many organizations find the variety dimension of big data a much bigger challenge than the volume or velocity dimensions… When asked about the dimensions of data organizations struggle with most, 49% answered variety, while 35% answered volume and 16% velocity.”

The ease of combining data from different sources with different structures and the availability of a standardized query language to work with the combination means that semantic technologies are well-suited to deal with data variety. The optional status of schemas means that instead of wrestling to coordinate schemas from different sources—a huge task in most data integration projects—you can start with only as much schema metadata as you want and then build from there to accommodate all of the data sources you want to incorporate into your system.

RDF offers several benefits over the relational model:

• Data does not need to be stored in tables. This means that you can start accumulating data and using it right away, with no need to plan out all the structure of all the data you might use first; it also means that when a system is in production, taking advantage of additional new data classes and properties does not require splitting and augmenting of tables and the necessary data dumps and reloads and query rewriting that often accompany schema evolution in relational databases.
• Integration of two different RDF datasets into a single one is almost trivial compared with the work of integrating two different relational databases into a single one, especially when the relational databases are stored using RDBMS systems from different vendors.
• SPARQL is a much more standardized query language than SQL. While SQL is a standard, vendors often replace very basic commands with their own extensions, reducing portability.
• RDF has a more explicit schema language with a much smaller reliance on external documentation for developers to understand the schema work of others.