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The end of the scientific paper as we know it (or not...)

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Two talks in one: the first talk expanding on the great promises of nanopublications, the second talk pointing out why much of that is too difficult (and some of it wrong).

Published in: Science
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The end of the scientific paper as we know it (or not...)

  1. 1. The end of the scientific paper as we know it (in 4 easy steps) Frank van Harmelen (+ Paul Groth) VU Amsterdam
  2. 2. Reports on the death of the scientific paper have been greatly exaggerated Frank van Harmelen (+ Paul Groth) VU Amsterdam And how the Semantic Web makes it possible
  3. 3. Semsci 2017 workshop • 7/10 papers about data • 3/10 are about papers and they are about papers written by&for people Thanks (in order of appearance) to: • Paul Groth • Tobias Kuhn • Jan Velterop • Barend Mons • Anita de Waard • Carole Goble
  4. 4. Scientific publishing hasn’t changed in 350 years • Letter from Christian Huygens (1652) • Writing to his prof in Mathematics • Citing (and complaining about) work of Descartes • One of 3000 letters by Huygens
  5. 5. 2017: Only superficial changes • Different format & style • Different medium (Web, PDF) • Different speed (PubMed = 2 papers/min)
  6. 6. Section 1: Related work Section 2: Research question Section 3: Experimental design Section 4: Experimental findings Section 5: Interpretation, conclusions And our papers still follow this storyline: Step 1: Study & interpret literature Step 2: Formulate hypothesis Step 3: Design experiment Step 4: Execute experiment Step 5: Publish results This storyline is important, but only readable by people, not for machines
  7. 7. How to make our papers more usable? “We only need information extraction because we first did information burial” (Barend Mons) “A journal paper is a state-funeral for your results” (Hans Akkermans)
  8. 8. Step 1: explicit rhetorical structure Capture the roles of blocks of text & make these roles explicit 1 paper = 1 Network of blocks N papers = 1 Network of blocks Results Results Interpretati ons Interpretati ons Conclusio ns Problem Method Results Interpretati ons Conclusio ns Problem Method One paper Another paper
  9. 9. Step 2: explicit fine-grained rhetorical structure Locate individual knowledge items and their relationships Example: Scholonto, ClaiMaker [Buckinham-Shum] Paper = set of claims Claim = text – relation – text Relation = causes, predicts, prevents; addresses, solves equals, is-similar-to; proofs, supports, challenges 1 paper = 1 fine-grained network of relations N papers = 1 fine-grained network of relations
  10. 10. Step 3: do away with the paper altogether. • Any fact is a relation between two things (“triple”) • Count each fact as a nano-publication • Together, these nano-publications form a huge very fine-grained network of relations, a web of knowledge, a “semantic web” • Computers as colleagues, not (only) tools Just publish the facts
  11. 11. What is a Nanopublication “A nanopublication is the smallest unit of publishable information: an assertion about anything that can be uniquely identified and attributed to its author” http://nanopub.org
  12. 12. Step 4: turning context into a 1st class citizen • Link to all the stuff that goes on before publication: – Datasets, workflows – Open Lab books – Open peer reviewing • Link to all the stuff that goes on after publication: – Websites – Blogs – Emails – Tweets
  13. 13. – Give web-addresses to objects (URIs) – Use the web to link between the objects – Provide meaning in a form that computers can handle (RDF) These principles embodied in already deployed technology We can build this using semantic web technology
  14. 14. So now we have… No longer a set of disconnected monolithic PDFs A network of facts, reviews, evidence, opinions, data
  15. 15. The story so far… • Publishing hasn't changed for 300+ years • The structure and format of our papers is still based on this • Deconstruct the scientific paper – from monolithic block of text – to a network of computer readable facts & context • All of this made possible by the semantic web
  16. 16. But…….
  17. 17. Pragmatic infeasiblility
  18. 18. Pragmatic infeasiblility Previous experiments in formalising (social) science turned out to be very hard: • Hannan and Freeman's theory of organizational inertia in first-order logic American Sociological Review 59(4):571-593 · August 1994 • Caroll & Hannan’s resource portioning theory in first order logic Computational & Mathematical Organization Theory 7, 87–111, 2001.
  19. 19. Pragmatic (in)feasiblility Many sciences are quantitave, but I guess this is still possible in RDF + MathML:
  20. 20. Pragmatic infeasiblility Science is a social activity, which includes persuasion, rhetorics, deliberate ambiguity, etc.
  21. 21. Issue #3: hedging
  22. 22. s
  23. 23. CACM, Vol. 22, No. 5, May 1979 “A proof doesn't settle a mathematical argument. Contrary to what its name suggests, a proof is only one step in the direction of confidence. We believe that, in the end, it is a social process that determines whether mathematicians feel confident about a theorem.
  24. 24. Thomas, J., The Axiom of Choice, North-Holland, Amsterdam, 1973 (a historical review of independence results in set theory)
  25. 25. Technical infeasibility: Scalability Scalability #statements/year = #statements/nanopub x #nanopubs/paper x #papers/year = 30 x N x 1.5M = N x 45M/yr Let’s hope N ≈ O(10)….
  26. 26. Technical infeasibility: expressivity • RDF hopelessly simple • Needs at least DL: “Mosquito’s transmit malaria“ All? no. Some? yes. Only? probably. transmit. Malaria  Mosquitos Many? Most? • Beyond DL: Probabilities, fuzziness, inconsistencies
  27. 27. Technical (in)feasibility: Argumentation graphs Escilatopram does not inhibit CYP2D6” Micropublications, Clark, Ciccarese, Goble, 2013
  28. 28. Technical (in)feasibility: Argumentation graphs Argumentation graphs require: • Defeasible logic • Modal logic • Higher-order logic • …. at scale of 450M statement/yr 
  29. 29. Should we give up on computers as scientific colleagues? • A more modest role for nano-publications? – Annotations of datasets? – Very approximate annotations of papers? • Make them speak our language instead of us speaking theirs?

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