Login | Register   
RSS Feed
Download our iPhone app
Browse DevX
Sign up for e-mail newsletters from DevX


How RDF Beats Basic XML: Web-Based Data Sharing : Page 3

The XML format provides the flexibility to describe anything, but it is also prone to errors and miscommunication. Find out how Resource Description Framework (RDF) can be a solution to these limitations.


Organizing Existing Data

Creating new content in RDF is great, but what about existing content and the content that will be created using the Web 2.0 methodology? The answer lies in Natural Language Processing. NLP is a science that deals with natural languages (the languages people speak) and computer languages.

There are two general types of NLP systems:

  1. Ones that convert information from software and data storages into human-readable form
  2. Ones that convert natural language data into machine-readable form

In order to categorize and finally organize the nearly 20 years' worth of data that aimlessly floats around on the web today, NLP systems can go through that data, make sense of it, and categorize it. These systems ultimately will help to convert all the old data into RDF, enabling it to be infinitely shared by computers on the web.

Not Only Categorization, but Reasoning

The unified RDF format's machine-readability allows machines to "make sense" of the data. While people can may look at birds and the sky and associate the two together, computers have to be instructed that birds and the sky belong together. Once they are made aware of that association, however, computers can incorporate it into their existing knowledge.

This has very interesting implications. If computers "understand" things by linking their logical associations, they can also "figure out" things that are locally associated. For example, if:

  1. All humans are mortal, AND
  2. Socrates is a human, THEN the computer can draw the conclusion that
  3. Socrates is mortal.

This process is called inference and it is widely used in RDF. More strictly, inference is a mathematical process of taking a set of axioms and asserting new logical consequences from them. In short, it is a way to get additional data from existing data. Many organizations use this concept and purposely structure their data in order to get new interesting data that can benefit their businesses.

Barriers to Full Adoption

All these solutions to existing problems, new inference technologies, uses of ontologies–why hasn't all this goodness been fully adopted yet?

Well, Semantic Web is quite an advanced computer science topic. This very introductory article alone touched on many new technologies, and each of these technologies has a learning curve. Additionally, the Semantic Web is only an extension of the web so Web 2.0 systems can still function without it. Before Web 3.0 reaches critical mass, it remains a luxury that only very well funded projects can afford to implement.

Alex Genadinik is the founder of San Francisco Hiking Community and a Startup Consultancy. Please say hello and continue the conversation on this topic on Twitter @genadinik
Comment and Contribute






(Maximum characters: 1200). You have 1200 characters left.



Thanks for your registration, follow us on our social networks to keep up-to-date