I believe @greglinden had some thoughts on solving the "Harry Potter problem" (very popular books always show up) when he worked on the Amazon recommendation algorithm. Seems related.
Since big scientific discoveries/inventions sometimes begin from obscure, poorly appreciated observations, it'd be great to have a search engine for the most important, but unpopular, facts. An anti-Google, if you will. Is such a thing possible?
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In a related vein,
@dabacon and Patrick Hayden had thoughts (more than thoughts?) about creating a virtual "Journal of Underappreciated Gems". Don't know if anything ever came of it, but it's a nice idea. Signals for sleeping beauty ideas need to be better understood! -
have you guys ever played with omnity? not a direct solution to ed's request but it had some pretty cool results on finding strangely correlated data as a sort of anti-search engine/ non- traditional NLP playhttps://techcrunch.com/2016/12/14/omnity-search-engine-finds-documents-relevant-to-yours-regardless-of-language/ …
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i used to have *a lot* of fun uploading papers into it.
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Never heard of it, interesting. Reminds me a bit of an old emacs plugin (from an MIT thesis) that would monitor what you're typing, and find relevant documents, just for stimulus: http://alumni.media.mit.edu/~rhodes/Papers/remembrance.html …
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Tried it ~10 years ago, was great fun. I suspect that with today's much, much better machine learning something like that would work a lot better.
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it's such a cool idea!
@eboyden3 you should incorporate this feature Michael just sent the link to in a very dope new version of Beagle :) - 2 more replies
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Defining importance by popularity (or amount of links as per Google algorithm) is straightforward. Defining importance of unpopular things is by definition purely subjective, so how would it work?
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I would model it on the design of *the people* in Bell Labs and Xerox PARC. Find interesting people (NOT those who are “popular”) who are passionate and can ship. Track what they do and say, and cluster those results. You will find other interesting people and ideas that way.
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This is how I tune Twitter. Find interesting people. Look and see who they are listening to, and follow them. Pay attention to what they say and ship. Interate.
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Perhaps it would be possible with some form of advanced semantic search. If a search could be intuitively constructed based on "concepts" (however that might be defined) rather than keywords, results could be sorted/filtered by prevalence in the literature.
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The literature itself would also have to be semantically analyzed in order to be searched, which sounds like quite a challenge. For what it's worth, I'm a software engineer with experience in app development. I'd be happy to connect if you're interested
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A somewhat related idea - querying for papers/concepts that are heavily relied upon, but have a small number of supporting papers. Almost like a science dependency tree, ordered by support/reliance ratio.
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I've had this idea for a NLP+AI knowledge base that reads literature and can infer relationships between molecules/biological entities/high-level concepts. In this case, "interesting/unpopular" ideas might be orphan nodes with few connections to dense, popular nodes
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Love this idea. Nice challenge. Benchmarking sets would be fun to collect ( including negs of course)
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It's tricky to separate them from all the unimportant, unpopular facts (and non-facts)...
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That's called an "article" by a "writer" who is an "expert"
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I think that would be easier to do by overlaying an identity graph on top of the documents graph. I.e. track who took interest in an obscure paper, if it's a 'rebel academic' in a given field, it might be signal for just what you're looking for.
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sometimes r/TodayILearned is a pretty good proxy for that! ;)
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