Scientific information is often hidden or not published properly. The ContentMine is a Social Machine consisting of semantic software and communities of domain expertise; it aims to liberate all scientific facts from the published literature on a daily basis. The talk , delivered to the Computational Institute, will be /was followed by a hands-on workshop learning how to use the technology and work as a community.

1. ContentMine: Open data
And Social Machines
Peter Murray-Rust
,
Computation Lab, Univ of Chicago, 2014-11-12

2. ContentMine: We use machines to
liberate[1] 100 million facts /yr from
the scientific scholarly literature and
make them free for everyone
(WikiData)
WikiData and ContentMines are social
machines
There are no longer any technical
obstacles, only people.
[1] Friday workshop: build your own social machine: scraping XML,

3. Liberation Software

4. http://en.wikipedia.org/wiki/Tim_Berners-Lee
Everything in this presentation is ODOSOS
(Open Data, Open Standards, Open Source)
CC0, CC-BY, W3C etc., Apache2, etc.
Open = “Free to use, re-use and redistribute
http://contentmine.org
http://bitbucket.org/petermr
http://wwmm.ch.cam.ac.uk
A promise: I (Petermr) will never sell out to non-transparent organizations.

5. http://www.budapestopenaccessinitiative.org/read
… an unprecedented public good. …
… completely free and unrestricted access to [peer-reviewed
literature] by all scientists, scholars, teachers,
students, and other curious minds. …
…Removing access barriers to this literature will
accelerate research, enrich education, share the
learning of the rich with the poor and the poor with
the rich, make this literature as useful as it can be, and
lay the foundation for uniting humanity in a common
intellectual conversation and quest for knowledge.
(Budapest Open Access Initiative, 2003)

6. Scientific and Medical publication (STM)[+]
• World Citizens pay $400,000,000,000…
• … for research in 1,500,000 articles …
• … cost $300,000 each to create …
• … $7000 each to “publish” [*]…
• … $10,000,000,000 from academic libraries …
• … to “publishers” who forbid access to 99.9% of
citizens of the world …
[+] Figures probably +- 50 %
[*] arXiV preprint server costs $7 USD per paper

7. petermr: I believe in Wikipedia
• 2006 http://en.wikipedia.org/wiki/User:Petermr
• 2006 started Open Data (term unknown then!)
• 2009: “the bit of Wikipedia that I wrote is correct” [challenging the
idea of “WP is junk”]
• 2009: “Wikipedia is the digital library of this century”
• 2012: I alert WP that Springer has copyrighted > 1000 of our
images [Springergate]
• 2014: “For facts in maths, physical and biological sciences I trust
Wikipedia.” (Wikimania2014)

8. A meritocratic
critical
volunteer
community

9. Volunteer community in chemistry: Open Data/Source/Standards

10. 4 Billion USD on human genome
yielded 800 Billion USD and 4 M job-years

11. Gloom Warning

12. Bad publication wastes science
…three problems—flawed design, non-publication,
and poor reporting—together
meant >85% of research funds were wasted, a
global total loss >100 billion USD per year.
[Lancet 2009]
[Even more] waste clearly occurs after
publication: from poor access, poor
dissemination, and poor uptake of the findings
of research. [PLOS Medicine 2014-05-27]

13. Publishers’ PDFs destroy science
PDFs do not contain words
or subscripts!
PDFs do not contain tables
and do not have columns
SVG is turned into JPEG because it’s easier to process

14. Elsevier wants to control Open Data
[asked by Michelle Brook]

15. Licences destroy Content Mining
WE WALKED OUT
• Brit Library
• JISC
• RLUK
• OKFN
• …
• Ross Mounce
• PM-R
STM Publishers Licence
2012_03_15_Sample_Licence_Text_Data_Mining.pdf
(Summary: PMR has NO rights)
• [cannot publish to: ] “libraries, repositories, or archives”
• [cannot] “Make the results of any TDM Output available on an externally facing server or
website”
• “Subscriber shall pay a […] fee”
Heather Piwowar: “negotiating with publishers [made me physically ill]”

16. CLOSED ACCESS MEANS PEOPLE DIE
CLOSED DATA MEANS PEOPLE DIE

17. Happiness Restored

18. The scientist’s amanuensis
• "The bane of my life is doing things I know computers could do
for me" (Dan Connolly, W3C)
Example: A semantic amanuensis could
• Give me a daily digest of mineralogy papers
• Extract all the crystal structures from them
• Compute physical properties with GULP and NWChem
• Compare the results statistically
• Preserve and distribute the complete operation
• Prepare the results for publication
The semantic web is having a personal amanuensis

19. Artificial Intelligence in science
In 1970 chess and chemistry were the sandboxes for AI. Some
approaches:
• Lookup (Knowledge)
• Natural Language Processing (NLP)
• Brute force calculation (inc. physical methods)
• Tree-pruning and heuristics
• Logic (cf. OWL-DL)
• Human-machine integration (crowdsourcing)
• Computer Vision
Domain-specific Turing test: Can a machine pass a first-year
chemistry exam?

20. The Semantic Web
"The Semantic Web is an extension of the
current web in which information is given well-defined
meaning, better enabling computers
and people to work in cooperation."
Tim Berners-Lee, James Hendler, Ora Lassila, The
Semantic Web, Scientific American, May 2001
CC-BY-SA Images from Wikipedia

21. Linked Open data from Wikipedia
“Which Rivers flow into the Rhine and are longer
than 50 kilometers?” or “Which Skyscrapers
in China have more than 50 floors and have
been constructed before the year 2000?”
Open Crystallography?
“Which countries where tropical diseases are
endemic have published structures of chiral
natural products?”
CC-BY-SA from Wikipedia

22. The Right to Read is the Right to Mine
http://contentmine.org

23. • Science can be read and understood by
human-machine Amanuensis-symbionts.
• Amanuenses are based on Wikipedia,
databases and software (e.g. ContentMine’s
AMI)
• The results are fed back into WP and WikiData
http://en.wikipedia.org/wiki/Eric_Fenby http://en.wikipedia.org/wiki/Symbiosis

24. Machine Extraction of scientific facts
• Crawl scientific literature
(Open Bibliography)
• Scrape each scientific article
(ContentMine-quickscrape)
• Extract the facts (ContentMine-AMI)
• Index (Wikipedia)
• Republish (WikiData)

25. RSU: Richard Smith-Unna
PMR: Peter Murray-Rust
CL: CottageLabs
Queues
Repos
Scientific
literature
Science
Plugins
Science
Volunteers

26. Linked Open Data – the world’s knowledge
GOV.uk
very little physical science 
DBPedia
BIO
http://upload.wikimedia.org/wikipedia/commons/3/34/LOD_Cloud_Diagram_as_of_September_2011.png
Lib
Comp
PDB
Ontologies
GOV
Music,
Art
Literature
Social
Knowledge
bases
RDF
triples

27. Part of a COD RDF entry
The Semantic Web understands this

28. Mathematics Markup Language
Energy of c.c.p lattice of argon
Human-friendly 4 pages clipped
Machine-friendly
Many editors and tools exist
We used MathWeaver
Automatic!
MathML

29. CML (Chemical Markup Language)
Automatic!
Human-friendly Machine-friendly

30. Innovation with Componentisation
Individual, manual,
unreusable, flaky
Commodity, standard,
reliable, re-usable

31. Current scientific information flow
… is broken for data-rich science
Non-semantic
data
Human input
Data extraction
difficult and
incomplete
Human
readers
PDF
Lineprinter output
Text files

32. Semantic network closes the loop
Data mined from
document
Computation
Measurement
Semantic
Authoring
Community
Data available for
e-science and re-use
Analysis

33. The network grows autonomously
Machine-machine
Human-machine
Machine-human
Human-human

34. Humans and machines use different
languages

35. How a machine reads a chemical thesis
nodes are compounds; arrows are reactions

36. Human-machine symbionts can read science!
WP_Lion
WP_Aspergillus_oryzae
WP_Soybean

37. With Wikipedia everyone can be a scientist
Facts Marked by “non-scientists” in ContentMine workshops

38. “nuggets” in a scientific paper
project places
quantity
units
Value ranges
chemical
Humans aren’t designed to mine this … 

39. Parsing chemical sentences
A FACT, uncopyrightable, and representable by triples

40. http://wwmm.ch.cam.ac.uk/chemicaltagger
• Typical
Typical chemical synthesis

41. Open Content Mining of FACTs
Machines can interpret chemical reactions
We have done 500,000 patents. There are >
3,000,000 reactions/year. Added value > 1B Eur.

42. We can’t turn a hamburger into a cow
But we can now
turn PDFs into
Science

43. UNITS
TICKS
QUANTITY
SCALE
TITLES
DATA!!
2000+ points

44. Dumb PDF
CSV
Semantic
Spectrum
Automatic
extraction
Takes < 1 second
Gaussian
Filter
2nd Derivative

45. Chemical Computer Vision
Raw Mobile photo; problems:
Shadows, contrast, noise, skew, clipping

46. Binarization (pixels = 0,1)
Irregular edges

47. Thinning: thick lines to 1-pixel

48. Chemical Optical Character Recognition
Small alphabet, clean typefaces, clear boundaries make
this relatively tractable. Problems are “I” “O” etc.

49. AMI https://bitbucket.org/petermr/xhtml2stm/wiki/Home
AMI reads the complete diagram,
recognizes the paths and
generates the molecules. Then
she creates a stop-fram animation
showing how the 12 reactions
lead into each other
CLICK HERE FOR ANIMATION
(may be browser dependent)
Example reaction scheme, taken from MDPI Metabolites 2012, 2, 100-133; page 8, CC-BY:

50. AMI Demo
http://www.mdpi.com/2218-1989/2/1/39/pdf
https://bitbucket.org/AndyHowlett/ami2-poc
ami2-poc -i example
-v org.xmlcml.xhtml2stm.visitor.chem.ChemVisitor
May take time to start if not connected to web
Output:./target/output/reactionsexample/
SVG: ./page1annotated.svg
CML: image.g.1.4.svg.reaction0.cml
Avogadro
Viewer:

51. Bacterial WP_phylogenetic tree
Genbank ID
American Type
Culture Collection
WP: Clostridium_butyricum
Our machines have read and interpreted 4300 in an hour with > 95% accuracy
Trees From http://ijs.sgmjournals.org/ used under new UK legislation (Hargreaves)

52. http://en.wikipedia.org/wiki/Digital_image_processing
http://en.wikipedia.org/wiki/Newick_format http://en.wikipedia.org/wiki/Phylogenetics
((n122,((n121,n205),((n39,(n84,((((n35,n98),n191),n22),n17))),((n10,n182),(
(((n232,n76),n68),(n109,n30)),(n73,(n106,n58))))))),((((((n103,n86),(n218,(n
215,n157))),((n164,n143),((n190,((n108,n177),(n192,n220))),((n233,n187),
n41)))),((((n59,n184),((n134,n200),(n137,(n212,((n92,n209),n29))))),(n88,(n
102,n161))),((((n70,n140),(n18,n188)),(n49,((n123,n132),(n219,n198)))),(((
n37,(n65,n46)),(n135,(n11,(n113,n142)))),(n210,((n69,(n216,n36)),(n231,n1
60))))))),(((n107,n43),((n149,n199),n74)),(((n101,(n19,n54)),n96),(n7,((n139
,n5),((n170,(n25,n75)),(n146,(n154,(n194,(((n14,n116),n112),(n126,n222)))
)))))))),(((((n165,(n168,n128)),n129),((n114,n181),(n48,n118))),((n158,(n91,(
n33,n213))),(n87,n235))),((n197,(n175,n117)),(n196,((n171,(n163,n227)),((
n53,n131),n159)))))));
(http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0036933 –
“Adaptive Evolution of HIV at HLA Epitopes Is Associated with Ethnicity in Canada” .

53. Open notebook science is the practice of
making the entire primary record of a research
project publicly available online as it is
recorded. (WP)
Jean-Claude Bradley was a chemist who
actively promoted Open Science in
chemistry,… He coined the term Open
Notebook Science. … A memorial
symposium was held July 14, 2014 at
Cambridge University, UK.[9]

54. RSU: Richard Smith-Unna
PMR: Peter Murray-Rust
CL: CottageLabs
Queues
Repos
Scientific
literature
Science
Plugins
Science
Volunteers

55. Thanks
• Shuttleworth Foundation and Fellowship
• Contentmine.org: Michelle Brook, Jenny Molloy,
Ross Mounce, Richard Smith-Unna,
CottageLabs, Charles Oppenheim
• Open Knowledge Foundation Community
• Wikimedia Community
• Blue Obelisk Community

56. My/our Dream
• An Open Bibliography of science, updated
daily
• An interface for ContentMine to feed new
facts into WikiData
• Domain-specific enthusiasts to create and run
fact extraction and validation
• Wikipedia to become a C21 publisher of
reference science