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Creating Semantic Lists at Ranker

By   /  June 2, 2011  /  No Comments

A recent interview with Ranker CEO and Founder Clark Benson takes a look at what how the semantic start-up is making the most of crowdsourcing.

A recent interview with Ranker CEO and Founder Clark Benson takes a look at what how the semantic start-up is making the most of crowdsourcing. Benson stated, “My whole life, I’ve been making lists of things… I also like the power of semantic data on the web, and the idea of linked data, so that you can really extract a lot of meaningful data and value from facts or opinions in a linked way. You can slice and dice information and pivot on almost anything, if that data is well-linked. The concept for Ranker came from merging the two together. The idea was to allow people to list and rank items, and use that to generate meaningful value as a recommendation engine. There are lots of things you can do with data if you can clearly organize it. The idea, is to take what has been unstructured data, and add structure to it.”

When asked how Ranker generates its data, Benson responded, “Ranker has two layers. First of all is the data layer, which is what I consider to be facts or objects. We actually source that data from Freebase and Factual. We grab large data sets of different things, such as movies, cars, books, or people, and then our interface adds what I call the opinion layer to that. Using our system, users can very quickly rank things or vote on thinks, vote on rankings of existing lists, and copy lists. It’s a very flexible, open platform, and is pretty open ended. We are able to take the Factual data so that users don’t have to type everything out, and they can very easily create content or make a list because most of it is already there in the system. They can search for things very quickly, drag and drop, add items to that list, and add their vote to our aggregated, crowdranked lists. That’s where the value really multiplies. Instead of having just one person’s opinion on a topic, we can aggregate thousands of people on a topic, and eventually filter by demographic, psychographics, and extract meaning in all kinds of ways.”

Read more here.

Image: Courtesy Ranker.com

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