Linked data — the ability to share and access related information within and between websites — is an emerging technology that’s already showing great promise. Current CMS capabilities are holding back adoption of linked data. Better tools could let content authors harness the power of linked data.
The value of linked data
Linked data is about the relationships between people, items, locations, and dates. Facebook uses linked data in its graph search, which lets Facebook users ask such questions as find “restaurants nearby that my friends like.” Linked data allows authors to join together related items, and encourage more audience interaction with content. Authors can incorporate useful, up-to-date info from other sources within content they create. Digital content that uses linked data lets audiences discover relevant content more easily, showing them the relationship between different items of content.
An outstanding example of what is possible with linked data is how the BBC covered the 2012 London Olympics. They modeled the relationships between different sports, teams, athletes, and nations, and were able to update news and stats about games across numerous articles that were available through various BBC media. With linked data, the BBC could update information more quickly and provide richer content. Audiences benefited by seeing all relevant information, and being able to drill down into topics that most interested them.
What’s holding back linked data?
Not many authors are familiar with linked data. Linked data has been discussed in technical circles for over a decade (it’s also called the semantic web — another geeky sounding term). Progress has been made to build linked data sets, and many enterprises used linked data to exchange information. But comparatively little progress has been made to popularize linked data with ordinary creators of content. The most ubiquitous application of linked data is Google’s knowledge graph, which previews snippets of information in search results, retrieving marked up information using a linked data format known as RDFa.
There are multiple reasons why linked data hasn’t yet taken off. There are competing implementation standards, and some developers are skeptical about its necessity. Linked data is also unfortunately named, suggesting that it concerns only data-fields, and not narrative content such as found on Wikipedia. This misperception has no doubt held back interest. A cause and symptom of these issues is that linked data is too difficult for ordinary content creators to use. Linked data looks like this:
According to Dave Amerland in Google Semantic Search, the difficulty of authoring content with linked data markup presents a problem for Google. “At the moment …no Content Management System (CMS) allows for semantic markup. It has to be input manually, which means unless you are able to work with the code…you will have to ask your developer to assist.”
It is not just the syntactical peculiarities of linked data that are the problem. Authors face other challenges:
- knowing what entities there are that have related information
- defining relationships between items when these have not already been defined
Improving the author experience is key to seeing wider adoption of linked data. In the words of Karen McGrane, the CMS is “the enterprise software that UX forgot.” The current state of linked data in the CMS is evidence of that.
Approaches to markup
Authors need tools to support two kinds of tasks. First, they need to mark up their content to show what different parts are about, so these can be linked to other content elsewhere that is related. Second, they may want to access other related content that’s elsewhere, and incorporate it within their own content.
For marking up text, there are three basic approaches to automating the process, so that authors don’t have to do mark up manually.
The first approach looks at what terms are included in the content that relate to other items elsewhere. This approach is known as entity recognition. A computer script will scan the text to identify terms that look like “entities”: normally proper nouns, which in English are generally capitalized. One example of this approach is a plug-in for WordPress called WordLift. WordLift flags probable entities for which there is linked data, and the author needs to confirm that the flagged terms have been identified correctly. Once this is done, the terms are marked up and connected to content about the topic. If the program doesn’t identify a term that the author wants marked up, the author can enter it himself.
A second approach to linked data markup is using highlighting, which is essentially manually tagging parts of text with a label. Google has promoted this approach through its Data Highlighter, an alternative to coding semantic information (a related Google product, the Structured Data Markup Helper, is similar but a bit more complex). A richer example of semantic highlighting is offered by the Pundit. This program doesn’t markup the source code directly, and is not a CMS tool —it is meant to annotate websites. The Pundit relates the data on different sites to each other using a shared linked data vocabulary. It allows authors to choose very specific text segments or parts of images to tag with linked data. The program is interesting from a UI perspective because it allows users to define linked data relationships using drag and drop, and auto-suggestions.
The third approach involves pre-structuring content before it is created. This approach can work well when authors routinely need to write descriptive content about key facets of a topic or domain. The CMS presents the author with a series of related fields to fill in, which together represent the facets of a topic that audiences are interested in. As Silver Oliver notes, a domain model for a topic can suggest what related content might be desired by audiences. A predefined structure can reveal what content facets are needed, and guide authors to fill in these facets. Pre-structuring content before it is created builds consistency, and frees the author from having to define the relationships between content facets. Structured modules allow authors to reuse descriptive narratives or multi-line information chunks in different contexts.
Limitations: use of data from outside sources
While authors may get better tools to structure content they create, they still don’t have many options to utilize linked data created by others. It is possible for an author to include a simple RSS-type feed with their content (such as most recent items from a source, or mentioning a topic). But it is difficult for authors to dynamically incorporate related content from outside sources. Even a conceptually straightforward task, such as embedding a Google map of locations mentioned in a post, is hard for authors to do currently. Authors don’t yet have the ability to mashup their content with content from other sources.
There may be restrictions using external content, either due to copyright, or the terms of service to access the content. However, a significant body of content is available from open sources, such as Wikipedia, geolocation data, and government data. In addition, commercial content is available for license, especially in the areas of health and business. APIs exist for both open source and licensed content.
Authors face three challenges relating to linked data:
- how to identify content elements related to their content
- how to specify to the system what specific aspects of content they want to use
- how to embed this external content
What content can authors use?
Authors need a way to find internal and external content they can use. The CMS should provide them with a list of content available, which will be based on the APIs the CMS is linked to. While I’m not aware of any system that let’s author’s specify external linked data, we can get some ideas of how a CMS might approach the task by looking at examples of user interfaces for data feeds.
The first UI model would be one where authors specify “content extraction” through filtering. Yahoo Pipes uses this approach, where a person can specify the source, and what elements and values they want from that source. Depending on the selection, Yahoo Pipes can be simple or complex. Yahoo Pipes is not set up for linked data specifically, and many of its features are daunting to novices. But using drag and drop functionality to specify content elements could be an appealing model.
Another Yahoo content extraction project (now open source) called Dapper allows users to view the full original source content, then highlight elements they would like to include in their feed. This approach could also be adapted for authors to specify linked data. Authors could view linked data within its original context, and select elements and attributes they want to use in their own content (these could be identified on the page in the viewer). This approach would use a highlighter to fetch content, rather than to markup one’s own content for the benefit of others.
Finally, the CMS could simplify the range of the linked data available, which would simplify the user interface even more. An experimental project a few years ago called SPARQLZ created a simple query interface for linked data using a “Mad Lib” style. Users could ask “find me job info about _______ in (city) _______. “ The ability to type in free-text, natural language requests is appealing. The information entered still needs to be validated and formally linked to the authoritative vocabulary source. But using a Mad Lib approach might be effective for some authors, and for certain content domains.
According to one view, most of the innovation in content management has happened, now that different CMSs largely offer similar features. I don’t subscribe to that view. As the business value of linked data in content increases, we should expect a renewed focus on intelligent features and the author experience. CMSs will need to support the framing of more complex content relationships. This need presents an opportunity for open source CMS projects in particular, with their distributed development structure, to innovate and develop a new paradigm for content authoring.