Google Assistant app on iPhone with the results of a “what time does dr. ruhlman office close” query. The results displayed include a card with “8:30AM–5:00PM” and the label, “Dr. Ruhlman Scott MD, Tuesday hours,” as well as links to call the office, search on Google, get directions, and visit a website. Additionally, there are four buttons labeled with the words “directions,” “phone number,” and “address,” and a thumbs-up emoji.
    This announcement was meant to help strategists, designers, and businesses get ready for the imminent growth of mobile. It continues to ring true for the age of linked data. With the prevalence of clever assistants and inquiries, an organization’s website is less inclined to be a visitor’s first encounter with rich content and less. Such as hours finding location info, phone numbers, and ratings –this engagement may be a user’s only interaction with an information resource.
    There is insufficient proof in this small sample to encourage a broad claim that calculations have”cognitive” prejudice, but even when we allow for possibly confounding variables, we can see the compounding problems we risk by dismissing structured content. No matter the Kaiser Permanente result we are given above for Dr. Donion is to get the wrong physician. What’s more, from the Google Assistant voice hunt, the discussion format doesn’t confirm whether we meant Dr. Donlon; it only provides us with her centre’s contact info. In these scenarios, providing clear, machine-readable content may only work to our benefit.

  • Designing Connected Content, Carrie Hane and Mike Atherton
  • In a content that was structured layout process, the connections between material chunks are specifically defined and described. This makes the relationships between them as well as both the material chunks legible into algorithms. Algorithms can then interpret a content package as the”page” I’m looking for–or remix and adapt the exact same content to provide me a list of directions, the amount of stars on a critique, the amount of time left until an office shuts, and any variety of other concise answers to certain questions.
    These components, when designed well, communicate relationships and data hierarchy visually to algorithms, and to readers. This structure allows Google Assistant to fairly surmise that the text from those

    headings represents payment options beneath the

    heading”Pay My Ticket.”
    Structured content is already a mainstay of various kinds of information on the internet. Recipe listings, for instance, have been based on structured articles for several years. While I hunt, by Way of Example,”bouillabaisse recipe” on Google, I’m provided with a regular list of links to recipes, as well as an overview of recipe measures, an image, and a pair of tags describing one example recipe:

    A combined HTML code editor and preview window showing markup and results for heading, ordered list, and list item HTML tags.

    Such quick interactions, but are only one part of a bigger problem: linked data is key to preserving the integrity of articles online. The associations I have used as examples, like the hospitals, government agencies, and colleges I have consulted with for decades, don’t measure the success of their communications efforts in ad clicks or page views. Success for these means linking community members, components, and patients with accurate information regarding the organization, wherever that information may be found and solutions. This definition of success readily applies to any sort of organization working to further its business goals.
    The model of anticipating users parse and to detect those pages to answer questions and building pages from the age that is pre-voice, is quickly becoming insufficient for communication. It precludes associations from participating in patterns of information discovery and seeking. And it may lead software agents to make inferences based on information that is insufficient or erroneous , potentially routing customers to competitors who communicate effectively.
    These outcomes aren’t just aggregated from sources, but are translated and remixed to supply a response. Getting instructions, placing a phone call, and accessing Dr. Ruhlman’s profile page on are all at the tips of my hands.

    Say, for example, that I want to collect more information about two recommendations I have been awarded for orthopedic surgeons.
    Design practices which build bridges between user requirements and technology requirements to fulfill company goals are critical to making this vision a reality. Information architects, content strategists, developers, and experience designers all have a part to play in designing and delivering content options.

    The City of Boston website's “How to Pay a Parking Ticket” page, showing a tabbed view of ways to pay and instructions for the first of those ways, paying online.
  • Content Everywhere, Sara Wachter-Boettcher
  • An enormous difference is read by the machine Though each of these elements would look the same to a human producing this page. While WYSIWYG text entry fields can support semantic HTML they fall prey to the idiosyncrasies of even the very well-intentioned content authors. By making content structure that is meaningful that a core element of a site’s content management system, organizations can produce semantically correct HTML for every component, every moment. This is also the foundation that makes it possible to capitalize on the rich relationship descriptions afforded by data that is connected.

    The business case for content that is structured layout

    Regardless of the visual simplicity of the Town of Seattle parking ticket page, it ensures that the integrity of its content across contexts as it is composed of content that is marked up semantically. “Buy My Ring” is a level-one heading (

    ), and every one of the options below it’s level-two headings (

    ), which indicate they are inferior to the level-one element.
    To be able to tailor results to such especially formulated questions, software agents have begun inferring intent and then using the data in their disposal. If I request Google Assistant what time Dr. Ruhlman’s office closes, for Example, it responds,”Dr. Ruhlman’s office shuts in 5 p.m.,” and displays this effect:

    Google Assistant app on iPhone with the results of a “what time does Doc Dr Stacey donion office close” query. The results displayed include a card with “8AM–5PM” and the label “MulitCare Neuroscience Center, Monday hours,” as well as links to call the office, search on Google, get directions, or visit a website.
    Google Assistant app on iPhone with the results of a “how do I pay a parking ticket in Seattle” query, showing nearly the same results as on the desktop web page referenced above.

    Along with locating and excerpting information, such as parking ticket payment options or recipe steps, software and search agent algorithms also aggregate content from several sources by using linked data.
    Although this use of semantic HTML offers distinct advantages over the”page display” styling we saw on the City of Boston’s website, the Seattle page also shows a weakness that’s typical of guide approaches to semantic HTML. You will notice that, at the Google Assistant results, the”Pay by Phone” option we saw on the web page wasn’t recorded. If we look at the markup of the webpage, we can see that while the three options found by Google Assistant are wrapped in both and

    tags,”Pay by Phone” is just marked up with an

    . This irregularity in semantic structure may be what is causing this option to be omitted by Google Assistant .

    Getting started: who and how

    The equivalent Google Assistant hunt offers a far more helpful effect than we see with Boston. In cases like this, the Google Assistant result links directly to the”Pay My Site” page and also lists a number of ways I can pay my ticket: online, by email, and also in person.
    The City of Seattle’s”Pay My Site” page, though it lacks the glistening visual design of Boston’s site, also communicates parking ticket options obviously to individual people:

    Google Assistant app on iPhone with the results of a “how do I pay a parking ticket in Boston” query, showing results only weakly related to the intended content.

    In this instance, we can see that Google is able to find lots of links to Dr. Donion in its own standard index outcomes, but it isn’t able to”know” the information about these sources well enough to present an aggregated outcome. In cases like this, the Knowledge Graph understands Dr. Donion is a Kaiser Permanente doctor, but it pulls in the wrong place and the wrong physician’s title in its attempt to build a Knowledge Graph screen.

    Software agent hunt and semantic HTML

    None of the links provided in this Google Assistant results take me directly to the”How to Purchase a Parking Ticket” page, nor do the descriptions definitely let me know I’m on the right track. (I did not ask about requesting a hearing) This is due to the fact that the content on the Town of Boston parking ticket page is designed to communicate content connections visually to readers but isn’t structured semantically in a manner that conveys those relationships to inquisitive algorithms.
    It’s likely that through repeated exposure to the research term”Dr. Stacey Donion,” Google Assistant fine-tuned the responses it supplied. The very first result, however, indicates that smart agents might be at least partially susceptible to the exact same availability heuristic that affects humans, wherein the information that’s simplest to remember frequently seems the most appropriate.

    The City of Seattle website‘s “Pay My Ticket” page, showing four methods to pay a parking ticket in a simple, all-text layout.

    You will also notice that while Dr. Stacey Donion is an exact match in all of the listed search results–which are numerous enough to meet with the initial results page–we are revealed a”did you mean” connection to get another physician. Stacy Donlon, MD, is a neurologist who practices. Multicare related profiles and does, however, provide semantic.
    The prevalence of voice for a manner of access to information makes supplying structured, machine-intelligible content more important. Voice and software agents that are intelligent are not just preventing users from their keyboards, they’re changing user behavior. Based on LSA Insider, there are many critical differences between voice inquiries and typed queries. Voice questions often be:
    In their recent book, Designing Connected Content, Carrie Hane and Mike Atherton define structured content as content that is”planned, designed, and connected outside an interface so it’s ready for any interface” A structured content design strategy frames content resources–like articles, recipes, product descriptions, how-tos, profiles, etc.–maybe not as pages available and read, but as packages composed of small chunks of content data that all relate to one another in meaningful ways.
    Rather than a selection of actions I’m presented to MultiCare Neuroscience Center.

Google search results page for Dr. Donion, showing a list of standard links for Dr. Donion, and a 'Did you mean: Dr Stacy Donlon MD' link at the top. There is a Google info box, as with the previous search results page example. But in this case the box does not display information about the doctor we searched for, Dr. Donion, but rather for 'Kaiser Permanente Orthopedics: Morris Joseph MD.'

This”featured snippet” perspective is possible since the content publisher,, has broken this recipe into the smallest meaningful chunks suitable with this subject matter and audience, then expressed advice about those chunks as well as the relationships between them at a machine-readable manner. In this example, has utilized both semantic HTML and connected data to produce this content not only a webpage, but in addition legible, accessible data which may be correctly interpreted, accommodated, and remixed by algorithms and intelligent agents. Let us look to see how they work together across inference contexts, and indexing, aggregation.
The pane on the right shows the values that are machine-readable.

Google search results info box for Dr. Ruhlman, showing an photo; a map; ratings; an address; reviews; buttons to ask a question, leave a review, and add a photo; and other people searched for.

Voice inquiries and articles inference

By communicating clearly in a digital context that includes inference and aggregation, associations are able to consult with their users where users actually are, be it on a search engine results page, a website, or even a digital helper. They are also able to keep control over the accuracy of their messages by ensuring the correct content hauled and are available across contexts.

In late 2016, Gartner called that 30 percent of web browsing sessions would be done with no display by 2020. Earlier the exact same year, Comscore had predicted that half of all searches are voice hunts by 2020. Even though there’s recent signs to suggest the 2020 picture might be more complicated than these broad-strokes projections imply, we are already seeing the effect that voice search, artificial intelligence, and intelligent software agents like Alexa and Google Assistant are creating on the way information can be consumed and found on the internet.

Linked data and content aggregation

HTML is both semantic and presentational because individuals know what lists and headings look like and mean, and calculations can comprehend them as elements with defined relationships.
Connected data expands the basic capabilities of semantic HTML by describing not only what sort of thing a page element is (“Pay My Ticket” is a

), but also the real-world concept that item represents: that

signifies a”pay action,” which inherits the structural characteristics of”trade actions” (the exchange of goods and services for cash ) and”actions” (activities carried out by an agent upon an object). Linked data creates a richer, more nuanced description and it provides the technical and structural advice that algorithms need to bring data together from disparate sources.

In this instance, Dr. Ruhlman’s profile is marked up with microdata depending on the language. This base that is content that is ordered provides the semantic foundation on. The Knowledge Graph info box, for instance, includes Google testimonials, which aren’t part of Dr. Ruhlman’s profile, but that have been aggregated into this review. The overview also includes an interactive map, made possible because Dr. Ruhlman’s workplace location is machine-readable.
As this page being read by a person, I easily understand what my options are for paying I will pay online, in person, by email, or on the telephone. However, things get a little confusing, if I ask Google Assistant to pay a parking ticket in Boston:

Practitioners from across the design community have shared a wealth of resources in recent years on creating material systems which work for algorithms and humans alike. To learn more about implementing a strategy for your organization, these articles and books are a great place to start:

The search for a second recommendation, MD, Stacey Donion, provides a very different experience. Like the City of Boston site over, Dr. Donion’s profile on the Kaiser Permanente website is perfectly intelligible to a sighted human reader. However, since its markup is completely presentational, its material is imperceptible to software agents.
HTML markup that concentrates just on the presentational aspects of a”page” may look perfectly fine to an individual reader however be completely illegible into an algorithm. If I Wish to find information about how to pay a parking ticket, a connection from the home page takes me straight to the”How to Pay a Parking Ticket” screen (scrolled to show detail):
Along with the indexing purpose that traditional search engines function, smart brokers and AI-powered search algorithms are currently bringing of obtaining information: inference and aggregation, modes. Because of this, design efforts that focus on creating pages that are visually effective are not enough to ensure accuracy or the integrity of articles published on the internet. Instead, by focusing on providing access to information in a structured, systematic manner that is legible to both humans and machines, content publishers can make sure that their content is equally accurate and accessible in these new contexts, whether they’re producing chatbots or tapping into AI directly. In this article, we will consider effect and the forms of material, and we’ll close with a set of tools which can help you get started with a content approach to data design.
In 2012, content strategist Karen McGrane wrote that”you do not have to determine which platform or device your clients use to get your content: they do.”

Google Structured Data Testing tool, showing the markup for Dr. Ruhlman's profile page on the left half of the screen, and the structured data attributes and values for the structured content on that page on the right half of the screen.
Google search results page for Scott Ruhlman, MD, showing a list of standard links and an info box with an image, a map, ratings, an address, and reviews information.

When we conduct Dr. Ruhlman’s Swedish Hospital profile site through Google’s Structured Data Testing Tool, we could see that content about him is structured as small, discrete elements, each of which is marked up with descriptive types and characteristics that convey both the meaning of those traits’ values and how that they fit together as a whole–all in a machine-readable format.

MultiCare Neuroscience Center, you’ll remember, is where Dr. Donlon–the neuroscientist Google thinks I might be searching for, not the surgeon I am actually searching for–practices.

Semantic HTML is markup that communicates information about the connections between document components, instead of simply describing how they ought to look on screen.

The City of Seattle website’s “Pay My Ticket” page, with the HTML heading elements outlined and labeled for illustration.
The City of Seattle website’s 'Pay My Ticket' page, with two HTML heading elements outlined and labeled for illustration, and an open inspector panel, where we can see that the headings look the same to viewers but are marked up differently in the code.

The Use of structured content