SPARQL with R in less than 5 minutes
From the post:
In this article we’ll get up and running on the Semantic Web in less than 5 minutes using SPARQL with R. We’ll begin with a brief introduction to the Semantic Web then cover some simple steps for downloading and analyzing government data via a SPARQL query with the SPARQL R package.
What is the Semantic Web?
To newcomers, the Semantic Web can sound mysterious and ominous. By most accounts, it’s the wave of the future, but it’s hard to pin down exactly what it is. This is in part because the Semantic Web has been evolving for some time but is just now beginning to take a recognizable shape (DuCharme 2011). Detailed definitions of the Semantic Web abound, but simply put, it is an attempt to structure the unstructured data on the Web and to formalize the standards that make that structure possible. In other words, it’s an attempt to create a data definition for the Web.
I will have to re-read Bob Ducharme’s “Learning SPARQL.” I didn’t realize the “Semantic Web” was beginning to “…take a recognizable shape.” After a decade of attempting to find an achievable agenda, it’s about time.
The varying interpretations of Semantic Web origin tales are quite amusing. In the first creation account, independent agents were going to schedule medical appointments and tennis matches for us. In the second account, our machine were going to reason across structured data to produce new insights. More recently, the vision is of a web of CMU Coke machines connected to the WWW, along with other devices. (The Internet of Things.)
I suppose the next version will be computers that can exchange information using the TCP/IP protocol and various standards, like HTML, for formatting documents. Plus some declaration that semantics will be handled in a future version, sufficiently far off to keep grant managers from fearing an end to the project.
The post is a good example of using R to use SPARQL and you will encounter data at SPARQL endpoints so it is a useful exercise.
The example data set is one of wildfires and acres burned per year, 1960-2008.
More interesting fire data sets can be found at: Fire Detection GIS Data.
Mapping that data by date, weather conditions/trends, known impact, would require coordination between diverse data sets.