Semantic Markup

We are seeing novel uses of markup languages, like publishing, syndication and even for desktop applications. Think about HTML, XML if you are uncomfortable with the markup languages phrase. There are different categories of markups - presentational (HTML), procedural (Tex, PostScript) and descriptive (XML).

The biggest advantage of a markup language is that it includes both - the data and information about the data, usually called the metadata. This metadata can be used for specifying various aspects of the data, like its presentation and behavior, and hence so many of its applications.

What Is Semantic Markup?

One more aspect of the data that can be specified using the metadata is its meaning. You can mark the data with the elements that specify what is the data about.

This also means that we need a markup language that can be extensible to express semantics. Nothing better than a descriptive markup language, like XML. XML brought to the table a culture of enclosing data between an open tag and a closed tag. Every element is composed of an open tag, data and a closed tag, e.g., <n1>Abhijit</n1> where Abhijit is the data and is enclosed between open and closed n1 tags.

Expressing the earlier example semantically would result into <name>Abhijit</name>.

In non-technical terms semantic language are labels; labels to identify the data enclosed. The strongest benefit of using semantics is illustrated nicely by Tim Bray at beginning of this article. The markup conveys meaning of the data. More importantly, the markup can be used to express relations. An apt example is that HTML cite tag can be used to specify source from where information is being cited.

The real intention of using Semantic Markup is to allow not only humans but even the machines to understand meaning of the data. For example, if the cite tag is globally used, a software program can be used to identify which source has been used by which article and create an inventory. In fact, Tim Berners Lee specified explicitly - semantic means machine processable.

Success of Semantic Markup

Semantic markup is not enforced. However, its success is affected by its uniformity and global use. Imagine if every individual came up with proprietary labels to identify data. Software program that works for one would not work for another, negating the benefit of Semantic Markup.

The ideal example to use is RSS, a feed format used for syndicating content. The basic idea of RSS would fail if feed for every web site had its own schema and tags. There would be a RSS parser for every web site that would not work for the other. The benefit lies in using the same labels for the same data. It should not be semantic only for you or me, it should be semantic for all.

This also leads to the point that descriptive markup language by itself is not semantic. Descriptive markup languages and schemas are only function as tools for creating Semantic Markup. None of them convey or hold the meaning that semantics are trying to convey.

That is why today we have XHTML, successor to HTML that is descriptive to express the semantics. However, XHTML by itself is not semantic, but it can be used efficiently to create Semantic markup.

The Larger Goal

The larger goal of using Semantic Markup is to build a Semantic Web, also called Web of data. In a simpler way, Semantic Web is about building knowledge by parsing through available information. This is exemplified by projects such as XFN which helps in representing relationships between individuals. This is one of the best ways of building a network of people.

Tim Berners Lee writes about his vision of the Semantic Web in his book Weaving The Web

I have a dream for the Web . . . and it has two parts.

In the first part, the Web becomes a much more powerful means for collaboration between people. I have always imagined the information space as something to which everyone has immediate and intuitive access, and not just to browse, but to create. [...] Furthermore, the dream of people-to-people communication through shared knowledge must be possible for groups of all sizes, interacting electronically with as much ease as they do now in person.

In the second part of the dream, collaborations extend to computers. Machines become capable of analyzing all the data on the Web - the content, links, and transactions between people and computers. A “Semantic Web,” which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy, and our daily lives will be handled by machines talking to machines, leaving humans to provide the inspiration and inuition. The intelligent “agents” people have touted for ages will finally materialize. This machine-understandable Web will come about through the implementation of a series of technical advancements and social agreements that are now beginning (and which I describe in the next chapter.)

The Semantic Web comprises of different technologies like RDF, XML and OWL. Again, Semantic Web is not necessary to build the knowledge, but it provides a standard way of doing it that everyone can follow.

Semantic Markup can make automation easier. One of the bigger problems in automation is that the data is not addressed the same everywhere. Semantic Markup can be used to build Semantic Web and software can be used for maximum benefit.