For the last several months, I’ve been engaged in an interesting project with Wikia. Wikia hosts hundreds of thousands of special-interest wikis for things as varied as pokemon, best cellphone rate comparisons, TV shows and Video Games.
Recently MediaWiki Deutschland started work on WikiData, an effort to use Semantic Web principles to create a factual knowledge base that can be used within Wikis (typically to replace Infobox information, which can vary between different language versions). This is a somewhat different direction than Semantic Media Wiki, which is more about using Wiki markup to express semantic relationships within a Wiki. As it happens JSON-LD is being considered as the data representation model for WikiData.
Linked Data at Wikia
As it turns out, Wikia has been quite interested in leveraging these tools. I did mention that Wikia is a for-profit company; one way they do this is through in-page advertising, but the amount of knowledge curated by the hundreds of thousands of communities is staggering. Unfortunately, native Wiki markup just isn’t that semantic. However, much of the information represented is factual (at least within the world-view of the wiki community).
To that end, I’ve been working on an experiment using JSON-LD and MongoDB to power a parallel structured data representation of much of the information contained in a wiki. The idea is to add a minimal amount of markup (hopefully) to the Wiki text and templates so that information can be represented in the generated HTML using RDFa. This allows the content of the Wiki to be mirrored in a MongoDB-based service using JSON-LD. Once the data has been freed from the context of the limited Wiki markup, it can now be re-purposed outside of the Wiki itself.
Knowledge modeling and data representation
Why use RDFa and not Microdata? The primary driver is the need to use multiple vocabularies to represent information. In my opinion, any new vocabulary needs to take into consideration schema.org; microdata works great with schema.org, and can generate RDF (see Microdata to RDF) as long as you’re constrained to a single vocabulary, don’t need to keep typed data, and don’t need to capture actual HTML markup. Unfortunately, any serious application beyond simple Search Engine Optimization (SEO) does need to use these features. In our case, much of the interesting data to capture are fragments of the Wiki pages themselves. Moreover, the content of any Wiki, much less one that has as much special meaning as, say, a Video Game, needs to describe relationships that are not natively part of the schema.org vocabulary. Schema does provide an extension mechanism partly for this purpose, and recently the ability to tag subjects with an additional type, not part of the primary vocabulary (presumably schema.org) was introduced. But, once the decision is made to use multiple vocabularies, RDFa has better mechanisms in place anyway.
At Wikia, we define a vocabulary as an extension to schema.org, that is, the classes defined within that vocabulary are sub-classes of schema.org classes, although typically the properties are not sub-properties of schema.org properties (we may revisit this). For example, a
wikia:VideoGame is a sub-class of
schema:CreativeWork, and a
wikia:WikiText is a sub-class of
schema:WebPageElement. There are additional class and property definitions to describe the structural detail common to Video Games in describing characters, levels, weapons, and so forth. An RDFa description will assert both the native class (e.g.,
wikia:VideoGame) and the schema.org extension class (e.g.
schema:CreativeWork/VideoGame). This allows search engines to make sense of the structured data, without the need to understand an externally defined vocabulary.
However, for Wikia’s purposes, and that of people wanting to work with in the Wikia structured-data echo-system, having a vocabulary that models the information contained within Wikia Wikis can be of great benefit. Key to this is knowning how much to model with classes and properties, and how much to leave to things such as naming conventions and keywords. In fact, there are likely cases where more per-wiki modeling is required, and we are continuing to explore ways in which we can further extend the vocabularies, without imposing a large burden on ontology development, and to keep the data reasonably generically useful.
Linked Data API
Although RDFa structured in HTML can be quite useful as an API itself, modern Single Page Applications are better served through RESTful interfaces with a JSON representation. JSON-LD was developed as a means of expressing Linked Data in JSON. It is fully compatible with RDF. Indeed, many of the concepts used in RDFa can be seen in JSON-LD – Compact IRIs, language- and datatyped-values, globally identified properties, and the basic graph data model of RDF.
Furthermore, a JSON-LD-based service allows resource descriptions, that may be spread across multiple HTML pages, to be consolidated into individual subject definitions. By storing these subject definitions in a JSON-friendly datastore such as MongoDB, the full power of a scaleable document store becomes available to the data otherwise spread out across numerous Wiki pages. But, the fact that the JSON-LD can be fully generated from the RDFa contained in the generated Wiki pages, ensures that the data will remain synchronized.
In the future, with the growth and adoption of systems such as WikiData, we can expect to see much of the factual information currently expressed as Wiki markup moved to outside services. The needs of the Wiki communities remain paramount, as they are at the heart of the data explosion we’ve seen in the hundreds of thousands of Wikis hosted at Wikia and elsewhere, not to mention WikiPedia and related MediaWiki projects.
As the communities become more comfortable with using knowledge stores, such as WikiData and Wikia’s linked data platform, we should see a further explosion in the amount of structured information available on the web in general. The real future, then, relies not only in the efforts of communities to curate their information, but in the ability to use the principles of the Semantic Web and Linked Data to infer connections based on distributed information.