1.
High throughput mining of the scholarly
literature: a new research tool
Peter Murray-Rust,
Dept of Chemistry and TheContentMine
MTO, Tilburg, NL, 2016-06-07
contentmine.org is supported by a grant to PMR as a

2.
The scholarly literature now produces 10,000 articles per day and it is essential to use machines to understand, filter
and analyse this stream. The full-text of these articles is much more valuable than the abstract, and in addition
many have supplemental files such as tables, images, computer code. Machines can filter this and extract
information on a huge and useful scale. Europe wishes to see this developed as a strategic area, but there is much
resistance from “rights-owners”.
The information in articles is in semi-structured form - a narrative with embedded data, even for some “data
files”. There is a huge amount of factual information in this material and many disciplines have journals whose
primary role is the reporting of facts - experimental protocols, formal observations (increasingly through instruments
or computation) , and analysis of results using domain-specific and general protocols. ContentMine, funded by the
Shuttleworth Foundation, has the vision of making these facts semantic and opening them to the whole world.
The two main activities of document analysis are Information Retrieval (IR) and Information Extraction (IE).
IR, filtering and classification, can be tackled by machine-learning (ML) or human-generated heuristics. ML is widely
used the drawbacks are: the need for an annotated corpus (boring, expensive in time, and difficult to update) and
the suspicion of “black-box” methods. Heuristics have the advantage that their methodology is usually self-evident
and can be crowd-sourced; however they are often more limited in which fields are tractable. IE is often domain- specific
(e.g. chemistry, phylogenetics) but there are general outputs which cover many disciplines. The most
tractable and common are typed numeric quantities in running text: “Thermal expansion and land glacier melting
contribute 0.15–0.23 meters to sea level rise by 2050, and 0.30 to 0.48 meters by 2100.” This is factual information
(it may or may not be “true”). Natural Language Processing (NLP) can extract the numeric quantites into
processable form. The terms (entities) “Thermal expansion”, “land glacier melting” are likely to be form a de facto
vocabulary. IE can also extract facts from tables, lists, and diagrams (graphs, plots, etc.). This is at an early stage,
but with probably 10-100 million numeric diagrams published per year the amount of data is potentially huge.
The major problems in exploiting this are sociopolitical. The major “closed” journals are concerned that this
will lead to “stealing” content and have therefore made it very difficult, technically and legally to mine scholarly
journals. The UK government passed an exception to copyright in 2014 which allows mining for non-commercial
research and ContentMine.org has been tooling up to support this.
PM-R and colleagues have legal access to a very wide range of scholarly publications and are interested in
exploring mutually beneficial research activities.
by Peter Murray-Rust
ContentMine.org and University of Cambridge
‘High throughput mining of the scholarly literature: a new research tool’

3.
Overview
• Scholarly literature
• Automation of downloading, normalization
• Discipline-dependent semantics/ontology
• Classification
• Extraction
• Annotation
• Mining diagrams
• Politics of mining

4.
The Right to Read is the Right to Mine**PeterMurray-Rust, 2011
http://contentmine.org

5.
(2x digital music industry!)

6.
Output of scholarly publishing
[2] https://en.wikipedia.org/wiki/Mont_Blanc#/media/File:Mont_Blanc_depuis_Valmorel.jpg
586,364 Crossref DOIs 201507 [1] /month 8000 papers/day
2.5 3 million (papers + supplemental data) /year
each 3 mm thick
 4500 m high per year [2]
* Most is not Publicly readable
[1] http://www.crossref.org/01company/crossref_indicators.html

7.
What is “Content”?
http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.01113
03&representation=PDF CC-BY
SECTIONS
MAPS
TABLES
CHEMISTRY
TEXT
MATH
contentmine.org tackles these

8.
http://www.nytimes.com/2015/04/08/opinion/yes-we-were-warned-about-
ebola.html
We were stunned recently when we stumbled across an article by European
researchers in Annals of Virology [1982]: “The results seem to indicate that
Liberia has to be included in the Ebola virus endemic zone.” In the future,
the authors asserted, “medical personnel in Liberian health centers should be
aware of the possibility that they may come across active cases and thus be
prepared to avoid nosocomial epidemics,” referring to hospital-acquired
infection.
Adage in public health: “The road to inaction is paved with research
papers.”
Bernice Dahn (chief medical officer of Liberia’s Ministry of Health)
Vera Mussah (director of county health services)
Cameron Nutt (Ebola response adviser to Partners in Health)
A System Failure of Scholarly Publishing

9.
CLOSED ACCESS
MEANS PEOPLE DIE

10.
Mining in action

11.
A recipe!
https://upload.wikimedia.org/wikipedia/commons/0/0b/Wikibooks_hamburger_recipe.png

12.
http://chemicaltagger.ch.cam.ac.uk/
• Typical
Typical chemical synthesis

13.
Automatic semantic markup of chemistry
Could be used for analytical, crystallization, etc.

14.
AMI https://bitbucket.org/petermr/xhtml2stm/wiki/Home
Example reaction scheme, taken from MDPI Metabolites 2012, 2, 100-133; page 8, CC-BY:
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)

15.
Tools and resources

16.
Europe PubMedCentral

17.
Dictionaries!

18.
Dengue Mosquito

19.
abstract
methods
references
Captioned
Figures
Fig. 1
HTML tables
abstract
methods
references
Captioned
Figures
Fig. 1
HTML tables
Dict A
Dict B
Image
Caption
Table
Caption
MINING
with sections
and dictionaries
[W3C Annotation / https://hypothes.is/ ]

20.
How does Rat find knowledge

21.
Disease Dictionary (ICD-10)
<dictionary title="disease">
<entry term="1p36 deletion syndrome"/>
<entry term="1q21.1 deletion syndrome"/>
<entry term="1q21.1 duplication syndrome"/>
<entry term="3-methylglutaconic aciduria"/>
<entry term="3mc syndrome”
<entry term="corpus luteum cyst”/>
<entry term="cortical blindness" />
SELECT DISTINCT ?thingLabel WHERE {
?thing wdt:P494 ?wd .
?thing wdt:P279 wd:Q12136 .
SERVICE wikibase:label {
bd:serviceParam wikibase:language "en" }
}
wdt:P494 = ICD-10 (P494) identifier
wd:Q12136 = disease (Q12136) abnormal condition that
affects the body of an organism
Wikidata ontology for disease

22.
Example statistics dictionary
<dictionary title="statistics2">
<entry term="ANCOVA" name="ANCOVA"/>
<entry term="ANOVA" name="ANOVA"/>
<entry term="CFA" name="CFA"/>
<entry term="EFA" name="EFA"/>
<entry term="Likert" name="Likert"/>
<entry term="Mann-Whitney" name="Mann-Whitney"/>
<entry term="MANOVA" name="MANOVA"/>
<entry term="McNemar" name="McNemar"/>
<entry term="PCA" name="PCA"/>
<entry term="Pearson" name="Pearson"/>
<entry term="Spearman" name="Spearman"/>
<entry term="t-test" name="t-test"/>
<entry term="Wilcoxon" name="Wilcoxon"/>
</dictionary>
“Mann-Whitney” link to Wikipedia entry and Wikidata (Q1424533) entry

23.
catalogue
getpapers
query
Daily
Crawl
EuPMC, arXiv
CORE , HAL,
(UNIV repos)
ToC
services
PDF HTML
DOC ePUB
TeX XML
PNG
EPS CSV
XLSURLs
DOIs
crawl
quickscrape
norma
Normalizer
Structurer
Semantic
Tagger
Text
Data
Figures
ami
UNIV
Repos
search
Lookup
CONTENT
MINING
Chem
Phylo
Trials
Crystal
Plants
COMMUNITY
plugins
Visualization
and Analysis
PloSONE, BMC,
peerJ… Nature, IEEE,
Elsevier…
Publisher Sites
scrapers
queries
taggers
abstract
methods
references
Captioned
Figures
Fig. 1
HTML tables
100, 000 pages/day
Semantic ScholarlyHTML
(W3C community group)
Facts
Latest 20150908

24.
Amanuens.is demo
These slides represent snapshot of an
interactive demo…
Subject: Flavour

25.
What plants produce Carvone?
https://en.wikipedia.org/wiki/Carvone
https://en.wikipedia.org/wiki/Carvone

26.
https://en.wikipedia.org/wiki/Carvone
WIKIDATA

27.
Carvone in Wikidata
Also SPARQL endpoint

28.
Search for carvone

29.
Mining for phytochemicals
• getpapers –q carvone –o carvone –x –k 100
Search “carvone”, output to carvone/, fmt XML, limit 100 hits
• cmine carvone
Normalize papers;
search locally for species, sequences, diseases, drugs
Results in dataTables.html
and results/…/results.xml (includes W3C annotation)
• python cmhypy.py carvone/ -u petermr <key>
send IUCN redlist plant annotations -> hypothes.is

30.
Annotation (entity in context)
prefix
surface
label
location
suffix

31.
ARTICLES FACETS
gene disease drug Phyto
chem
species genus words

32.
Remote &
Local papers
Disease
ICD-10
phytochemicals
species
Commonest
words

33.
Mining for phytochemicals
• getpapers –q carvone –o carvone –x –k 100
Search “carvone”, output to carvone/, fmt XML, limit 100 hits
• cmine carvone
Normalize papers;
search locally for species, sequences, diseases, drugs
Results in dataTables.html
and results/…/results.xml (includes W3C annotation)
• python cmhypy.py carvone/ -u petermr <key>
send annotations -> hypothes.is

34.
Annotation (entity in context)
prefix
surface
label
location
suffix

35.
Annotation sent to hypothes.is
prefix
suffix
source
user
text
uri
maybe 100+ annotations per paper
text

36.
Annotation with Hypothes.is

37.
Amanuens.is
Hypothes.is link
Hypothes.is markup
of article

38.
Annotation with Hypothes.is
Original publication “on publisher’s site”
Annotation
“on Hypothes.is site”

39.
Systematic Reviews
Can we:
• eliminate true negatives automatically?
• extract data from formulaic language?
• mine diagrams?
• Annotate existing sources?
• forward-reference clinical trials?

40.
Polly has 20 seconds to read this paper…
…and 10,000 more

41.
ContentMine software can do this in a few minutes
Polly: “there were 10,000 abstracts and due
to time pressures, we split this between 6
researchers. It took about 2-3 days of work
(working only on this) to get through
~1,600 papers each. So, at a minimum this
equates to 12 days of full-time work (and
would normally be done over several weeks
under normal time pressures).”

42.
400,000 Clinical Trials
In 10 government registries
Mapping trials => papers
http://www.trialsjournal.com/content/16/1/80
2009 => 2015. What’s
happened in last 6 years??
Search the whole scientific literature
For “2009-0100068-41”

43.
Mining diagrams

44.
Examples of plots

45.
Posterisation
Extracted since unique posterized colour

46.
Ln Bacterial load per fly
11.5
11.0
10.5
10.0
9.5
9.0
6.5
6.0
Days post—infection
0 1 2 3 4 5
Bitmap Image and Tesseract OCR

47.
“Root”

48.
OCR (Tesseract)
Norma (imageanalysis)
(((((Pyramidobacter_piscolens:195,Jonquetella_anthropi:135):86,Synergistes_jonesii:301):131,Thermotoga
_maritime:357):12,(Mycobacterium_tuberculosis:223,Bifidobacterium_longum:333):158):10,((Optiutus_te
rrae:441,(((Borrelia_burgdorferi:…202):91):22):32,(Proprinogenum_modestus:124,Fusobacterium_nucleat
um:167):217):11):9);
Semantic re-usable/computable output (ca 4 secs/image)

49.
Supertree created from 4300 papers

50.
But we can now
turn PDFs into
Science
We can’t turn a hamburger into a cow
Pixel => Path => Shape => Char => Word => Para => Document => SCIENCE

51.
UNITS
TICKS
QUANTITY
SCALE
TITLES
DATA!!
2000+ points

52.
Dumb PDF
CSV
Semantic
Spectrum
2nd Derivative
Smoothing
Gaussian Filter
Automatic
extraction

53.
C) What’s the problem with this spectrum?
Org. Lett., 2011, 13 (15), pp 4084–4087
Original thanks to ChemBark

54.
After AMI2 processing…..
… AMI2 has detected a square

55.
Politics

56.
http://www.lisboncouncil.net/publication/publication/134-text-and-data-mining-for-research-and-innovation-.html
Asian and U.S. scholars continue to show a huge interest in text and data mining
as measured by academic research on the topic. And Europe’s position is falling
relative to the rest of the world.
Legal clarity also matters. Some countries apply the “fair-use” doctrine, which
allows “exceptions” to existing copyright law, including for text and data mining.
Israel, the Republic of Korea, Singapore, Taiwan and the U.S. are in this group.
Others have created a new copyright “exception” for text and data mining – Japan,
for instance, which adopted a blanket text-and-data-mining exception in 2009, and
more recently the United Kingdom, where text and data mining was declared fully
legal for non-commercial research purposes in 2014. Some researchers worry that
the UK exception does not go far enough; others report that British researchers are
now at an advantage over their continental counterparts.
the Middle East is now the world’s fourth largest region for research on text and
data mining, led by Iran and Turkey.

57.
@Senficon (Julia Reda) :Text & Data mining in times of
#copyright maximalism:
"Elsevier stopped me doing my research"
http://onsnetwork.org/chartgerink/2015/11/16/elsevi
er-stopped-me-doing-my-research/ … #opencon #TDM
Elsevier stopped me doing my research
Chris Hartgerink

58.
I am a statistician interested in detecting potentially problematic research such as data fabrication,
which results in unreliable findings and can harm policy-making, confound funding decisions, and
hampers research progress.
To this end, I am content mining results reported in the psychology literature. Content mining the
literature is a valuable avenue of investigating research questions with innovative methods. For
example, our research group has written an automated program to mine research papers for errors in
the reported results and found that 1/8 papers (of 30,000) contains at least one result that could
directly influence the substantive conclusion [1].
In new research, I am trying to extract test results, figures, tables, and other information reported in
papers throughout the majority of the psychology literature. As such, I need the research papers
published in psychology that I can mine for these data. To this end, I started ‘bulk’ downloading research
papers from, for instance, Sciencedirect. I was doing this for scholarly purposes and took into account
potential server load by limiting the amount of papers I downloaded per minute to 9. I had no intention
to redistribute the downloaded materials, had legal access to them because my university pays a
subscription, and I only wanted to extract facts from these papers.
Full disclosure, I downloaded approximately 30GB of data from Sciencedirect in approximately 10 days.
This boils down to a server load of 0.0021GB/[min], 0.125GB/h, 3GB/day.
Approximately two weeks after I started downloading psychology research papers, Elsevier notified my
university that this was a violation of the access contract, that this could be considered stealing of
content, and that they wanted it to stop. My librarian explicitly instructed me to stop downloading
(which I did immediately), otherwise Elsevier would cut all access to Sciencedirect for my university.
I am now not able to mine a substantial part of the literature, and because of this Elsevier is directly
hampering me in my research.
[1] Nuijten, M. B., Hartgerink, C. H. J., van Assen, M. A. L. M., Epskamp, S., & Wicherts, J. M. (2015). The
prevalence of statistical reporting errors in psychology (1985–2013). Behavior Research Methods, 1–22.
doi: 10.3758/s13428-015-0664-2
Chris Hartgerink’s blog post

59.
WILEY … “new security feature… to prevent systematic download of content
“[limit of] 100 papers per day”
“essential security feature … to protect both parties (sic)”
CAPTCHA
User has to type words

60.
http://onsnetwork.org/chartgerink/2016/02/23/wiley-also-stopped-my-doing-my-research/
Wiley also stopped me (Chris Hartgerink) doing my research
In November, I wrote about how Elsevier wanted me to stop downloading scientific articles for my research. Today, Wiley
also ordered me to stop downloading.
As a quick recapitulation: I am a statistician doing research into detecting
potentially problematic research such as data fabrication and
estimating how often it occurs. For this, I need to download many scientific articles, because my research
applies content mining methods that extract facts from them (e.g., test statistics). These facts serve as my data to answer my research
questions. If I cannot download these research articles, I cannot collect the data I need to do my research.
I was downloading psychology research articles from the Wiley library, with a maximum of 5 per minute. I did this using the tool quickscrape,
developed by the ContentMine organization. With this, I have downloaded approximately 18,680 research articles from the Wiley library,
which I was downloading solely for research purposes.
Wiley noticed my downloading and notified my university library that they detected a compromised proxy, which they
had immediately restricted. They called it “illegally downloading copyrighted content
licensed by your institution”. However, at no point was there any investigation into whether my user credentials were
actually compromised (they were not). Whether I had legitimate reasons to download these articles was never discussed.
The original email from Wiley is available here.
As a result of Wiley denying me to download these research articles, I cannot collect data from
another one of the big publishers, alongside Elsevier. Wiley is more strict than Elsevier by immediately condemning the
downloading as illegal, whereas Elsevier offers an (inadequate) API with additional terms of use (while legitimate access
has already been obtained). I am really confused about what the publisher’s stance on content mining is, because Sage
and Springer seemingly allow it; I have downloaded 150,210 research articles from Springer
and 12,971 from Sage and they never complained about it.

61.
Julia Reda, Pirate MEP, running ContentMine
software to liberate science 2016-04-16

62.
The Right to Read is the Right to Mine**PeterMurray-Rust, 2011
http://contentmine.org