Linked Data projects in and of themselves are cool. But sometimes, one of them just stands out as even cooler. Such is the case with Linked Jazz, some 2900-triples strong in the service of identifying and revealing the network of social relationships among the jazz community.

Talk about all that jazz. The effort, led by Cristina Pattuelli, associate professor at the Pratt Institute School of Information and Library Science, includes a visualization tool developed by a graduate student there, Matthew Miller, that provides different and compelling ways to explore connections among the jazz greats and the lesser-knowns, as well. You can view individuals based on the number of connections they have, for instance, or on their shared connections.

“It can be used dynamically to click on an artist and see the pattern of all the connections around him, play a clip from YouTube, have a little bio,” Pattuelli says. It’s innovative, she says, because it runs direct from a browser.One of the first applications for the project, Pattuelli explains, was to create a directory of names of jazz artists. “There was not a vocabulary out there, let alone a Linked Open Data vocabulary,” she says. The Linked Jazz Name Directory consists of 8,725 unique names of jazz musicians as N-Triples. The Jazz Artist Names Mapping tool links dbpedia URIs and other name authorities, principally the US Library of Congress, to help mitigate issues that can occur around matching individuals among different authority systems.

“The project intended to use jazz interview transcripts of musicians from jazz archives, and we needed a way to identify people and the connections they shared,” she says. The prototype relied on 50 transcripts but that will be expanded so that users will have the comfort of an even higher level of network accuracy. And “we needed a tool to help us to extract and match the names of musicians from these text documents.” Edward Kennedy Ellington, for example, is also and probably more widely known as Duke Ellington. “So, integrating DBpedia names with other authority fields means we don’t just have the most popular name but link to all the variants,” she says.

Earlier this month saw the release of the latest version of Linked Jazz’s crowdsourcing tool, Linked Jazz 52nd Street, developed by Miller, which provides features such as expandable transcript excerpts, progress bars, and the ability for users to visualize the connections they are making between jazz musicians in real-time. The work to represent the connections among artists in a granular way would benefit with the help of the crowd.

“We want the jazz fanatics and researchers to identify exactly what kind of relationships [there are],” she says. Currently you can see that Thelonious Monk and Mary Lou Williams are connected, but the crowd sourcing tool provides  a mechanism for people to weigh in on how – and set the record straight on some details.

For example, Wikipedia tells you that Monk influenced Williams, she says, but “by analyzing the transcripts you see it was the other way around. So,  we will put these snippets, like small portions of transcripts, for people to see, and they can click from a list of possible relationships which was the correct one.” The tool aims to include a way to resolve conflicts and ultimately bring the correct information into the network.

There’s more than a cool factor to the project, though. The big picture behind why the culture needs a Linked Data-powered social network for jazz, she says, “is a tool like this can help scholars, researchers, and the general public to analyze the social history of jazz much more easily because you understand the types of connections held among jazz musicians. …It would provide you with an immediate view and understanding of which kind of dynamics there were among artists.”

Linked Jazz will expand in other ways, too, such as deriving from transcripts entities like producers who had important roles in jazz recordings, or clubs where the legends played.  “They are not there now but they have been identified,” she says.