Traditionally, data has been regarded as structured information, whereas content has been considered to be unstructured and free flowing. But as content has increasingly become digital, it needs to also be managed like data.
Structured content refers to the concept of organizing and treating digital content, such as web pages, images, and infographics like data. Content is managed as modular, discrete pieces of information and tagged with metadata that allow computers to understand the content better so that the information can be appropriately presented and searched. Structured content has the potential to transform how people find, understand, share, and use government information.
Content models define the structure of information products and their constituent content components. For example, an article may be marked up with a title, a description or a blurb, a byline, and topics. This structure can be used by presentation programs to display these content elements in different ways in different contexts or on different devices. In addition, they can be used by search engines to better understand the content which in turn leads to better search results.
Developing shared and open content models enable content producers to make their content more adaptive and shareable and “future-ready.” It also enables more sharing of content and the ability to mashup content from multiple sources.
These models define a core set of content elements. These models are not exhaustive and do not represent all the possible content elements that agencies may want to include. The models were based on a review of both examples of events and articles on federal websites and on existing schemas and standards such as Schema.org and RDF-A.
In 2013, the Digital Services Innovation Center sponsored a cross-agency working group to develop shared and open content models for digital content across the federal government. The Open and Structured Content Models Working Group members who contributed to the article and event models are listed below.
Allison Alexander
Logan Powell
Fred Smith
Daniel Munz
Jill James
Edward McCarthy
Mary Maher
Gong Chen
Robert Rand
Wayne Whitten
