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Driving MapReduce into the Semantic Web

According to an article published on Datanami, at the turn of the century, a revolution took place on the infant World Wide Web that transformed static pages into interactive, collaborative portals, spawning the social media era that permeates today. That transition was called Web 2.0, and there’s an open discussion about how semantic data will create the next transition. A group at the University of Freiburg is working on ways to enable MapReduce to pave the way.

At the 12th International Semantic Web Conference (ISWC) in Sydney, Australia later this month, researcher Alexander Schätzle says that his group will be presenting on PigSPARQL, a SPARQL query processing system built on top of MapReduce. This new system, they hope, will pave the way for the enablement of the semantic Web for people who are not MapReduce programmers.

For those who aren’t familiar with the concept of the semantic Web, it’s essentially machine learning for the World Wide Web. Using metadata and algorithms that help computers understand what content people are searching for, the machines can then MapReduce their way to delivering it.

It’s able to smartly do this using a family of W3C specifications known as the Resource Description Framework (RDF), which is essentially a metadata framework that can be used to identify and link data to other data, giving the machine intelligence a handle. While RDF (and its corollary RDF triples) are outside of the scope of this article, the important thing to understand is that RDF data is growing exponentially, and the potential for the semantic web is burgeoning before us.

 

By Isaac Lopez

Read full article here

Tagged in: data metadata
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EuropeLogo eInfastructure This project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 313203
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