Science and semantic web

on Friday, January 18th, 2013 7:04 | by Julien Colomb

I was on a meetup of corporate semantic web last Tuesday. These people are using semantic web technologies (making machine readable content based on ontological terms and relation between these terms) to improve the efficacy of private companies. For instance, they work on ways to improve wiki contents which may be produced in a company. This corresponds at using ontological term to annotate the wiki content and other related technologies. This can be used to find an expert in one category (=somebody who’s posts are rarely corrected on a specific subject).

What is the scientific community (the one which should be leading the way actually) doing during that time: we use text search in “keywords” and titles to find the appropriate literature, that we have to read thouroughly to drive our one conclusions about these different parts… At least, that is what we do 90% of the time, and we all know how inaccurate this can be. Experimental results may be translated into a machine readable content, why aren’t we doing it (it could make everything that much simpler, faster and more accurate)?

The answer: 1. there is no tool nor database where we could do it. 2. Scientists do not have the time to do it, they are over-pressurized to produce data, not to make it reusable or machine readable.
How to push people to use the semantic web technologies, how to ease this use, should it be done by the authors or by the community, pre or post publication, what ontology tool to use,… What can we do? Is anyone asking these questions around? Does a platform like researchgate be a way to introduce this, or should we go for a public solution, inside pubmed for example?

Is any of you asking/answering these questions?

By the way, this post is tagged by none-ontological terms, a shame?

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Category: open science, Uncategorized

One response to “Science and semantic web”

  1. People are asking these questions and attempting to develop the semantic capabilities in science. However, there is little incentive to maximize re-usability or machine-readability. Nobody cares whether your paper, your software or your data conform to any such requirements. You’re dead on: this must change!

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