Brownian thought space

Cognitive science, mostly, but more a sometimes structured random walk about things.

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Location: Rochester, United States

Chronically curious モ..

Saturday, September 23, 2006


Here's an idea. Imagine a Wiki; which would be a collection not of scientific papers, but paper summaries. Advanrtages:
  1. Quick outline views of published (or to-be-published) articles and manuscripts.
  2. Allows discussion of specific papers by anyone in an easy, online way: one Discussion section for registered, university affiliated (past or present) scientists, a second Discussion for unregistered people, interested laypeople
  3. Links to full-text sources, including pre-prints
  4. Not limited to particular journals, so gives a broad coverage
  5. Searchable either in journal mode, in a hierarchical, Dewey Decimal-style mode, a standard meta-information mode, or a special, heuristic-based clustering mode


The main plan of the organization is something like the Dewey Decimal system. Except, beyond some (arbitrary) deep level, things would be clustered by meta-data and by the overlap of the references.
Reference-based similarities
The main idea is that if you are quoting the same people, very likely you are talking about similar things. But, if paper X is published in 2005, and paper Y in 1997, then naturally, part of the non-overlap will be just due to the fact that the dates are different. So the obvious thing is to first remove all references after the smallest of the most recent dates from both papers. So here would be an algorithm: Computing Reference-based overlap for two papers X and Y:
  • Find MinDate = Min{MaxX, MaxY}, the smallest of the most recent dates of the two papers.
  • List {Ref(X')} and {Ref(Y')}, all the references <=MinDate.
  • Find some metric of overlap; e.g.
RefScore = (common elements in the two lists)/(total number of elements in the two lists) Clustering would be based on RefScore, shared Author lists, and Keywords.


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