Building on the success of Wolfram|Alpha, WDF uses the Wolfram Language and the Wolfram Knowledgebase to provide a standardized representation of real-world constructs and data.
WDF finally makes it practical to put a wide range of types of data in truly computable form:
Store WDF in files, databases, etc. or the Wolfram Cloud
Directly compute with WDF in the Wolfram Language
Wolfram Semantic server, etc.
Wolfram Data Science Platform, etc.
WDF is a human-readable plaintext format that can be rendered in JSON, XML and other forms.
WDF provides not just a language for representing real-world data, but defines actual canonical forms, based on knowledge about thousands of domains and millions of entities.
As by far the largest computable knowledge system ever built, Wolfram|Alpha has been in a unique position to construct and test a broad ontology—which is now exposed as the basis for WDF.
Through its use of the Wolfram Language, WDF can represent not just static ontological relationships, but also dynamic relationships that are defined by real-time computations.
WDF includes the world's most sophisticated system for handling units of measure, covering more than 10,000 named units.
WDF inherits from the Wolfram Language a well-developed system for handling precision of numbers.
Cities, stars, people, chemicals, products, mountains, species… : almost anything in the world with a commonly used public name is already assigned a canonical identifier in WDF.
WDF represents not just individual entities such as cities or planets, but also structures such as networks, time series, images and geometries.
Because WDF is based on the symbolic Wolfram Language, it can represent missing data in a flexible symbolic way.
WDF has full support for state-of-the-art high-precision geodesy, with all standard datums and projections.
The Wolfram Language—as accessed from a variety of Wolfram products and services—lets you convert from unstructured data, including free-form natural language, to precise canonical WDF.
6/23/88, june 23, '88, 4th thursday in june 1988, etc. Dates are a typical example of partially structured data, with many formats, all of which WDF tools let you automatically convert to a canonical WDF form.
WDF tools—like Wolfram|Alpha—can go from standard natural language to the semantic form needed for WDF, including performing knowledge-based computations.
WDF tools allow you to import large spreadsheets or databases, learning from collections of elements how best to interpret them, and render them into correct canonical WDF.
WDF tools include a giant lexicon for converting from common names to the canonical identifiers of WDF.
WDF tools have extensive linguistic disambiguation techniques, modeled on the highly successful methods used every day by Wolfram|Alpha.
The Wolfram Smart Field service lets you put an input field into any form and have it automatically interpreted as WDF using natural language understanding implemented by the Wolfram Cloud.
WDF provides a powerful way to represent data-level knowledge—and dovetails immediately with algorithmic knowledge as supported in the Wolfram Language.
Through being based on the Wolfram Language, WDF is completely human-readable—and can immediately be rendered with full formatting in CDF computable documents.
WDF can be rendered in the Wolfram Language or in formats like JSON and XML.
WDF tools maintain mappings between WDF entities and entities in sources like Wikipedia and IMDb.
Because WDF completes a whole semantic representation of data, you don't need to rely on footnotes to maintain information on how to interpret pieces of data.
The symbolic structure of WDF makes it easy to include arbitrary metadata.
WDF is fully extensible, with new private entities being assigned universally unique UUIDs, which can be mapped if desired into entities in the Wolfram Cloud.
Use WDF to ensure different parts of an organization understand pieces of data the same way.
Use WDF to create data-backed publications, in which data can immediately be used.
With WDF, you don't need to remember footnotes about data, and if all else fails, it's always human‑readable.
Use WDF to represent data derived from semantically smart input fields on forms.
Store data with meaning attached, for computation or later use.
WDF provides the connection between real-world measurements and abstract computation.
