Summary:

Sure, you can trust a site is delivering you the best search results, but sometimes it might be nice to dig down, see a little of what the system sees and find that needle in the haystack. A new semantic search interface might let that happen.

synapses

Chalk up another win for healthcare — and perhaps the entire publishing world — thanks to big data. A semantic analysis company called Atigeo has made it possible to search the archive of the National Institute of Health’s PubMed library, which consists of more than 400,000 research papers, using a graphical interface rather than just scrolling through pages of results.

Admittedly, I don’t spend too much time searching academic or professional databases anymore, but this is a novel approach from what I’ve seen. Powered by Atigeo’s software product called xPatterns, the new interface for exploring PubMed presents a hub-and-spoke-like diagram (which it calls “bubbles and sticks”) that viewers can manipulate by adding and subtracting search terms or by searching for related terms. With every click, a users drills down further into the results, although the original map of terms remains.

Atigeo markets xPatterns to a number of industries, from the public sector to advertising, to help them draw better connections between their data, but this use case is particularly cool. That’s because while semantic analysis is already used rather extensively to produce more-relevant search results (or just to proactively present users with content), it’s not every day someone rethinks the process of how we actually navigate search results. Given a little time for experimentation and acclimation, perhaps the xPatterns approach will catch on.

I don’t see why it has to be limited to scholarly databases either. I can see everyday web users wanting to parse through search results on their favorite content sites using a similar approach. Sure, you can trust a site is delivering you exactly what you want to see, but sometimes it might be nice to dig down, see a little of what the system sees to find that needle in the haystack.

Feature image courtesy of Shutterstock user Michelangelus.

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