This document summarizes content mining technology and policy developments. It discusses what content and mining are, provides a brief history of content mining, and outlines legal considerations around copyright and database rights. It then describes the ContentMine software and pipeline for scraping, normalizing, and extracting facts from scholarly documents at scale. Examples of mining applications in chemistry, clinical trials, phylogenetics, and genome annotation are provided. The document concludes with a discussion of the potential value of content mining for public health researchers.

1. Content Mining:
Technology and Policy Developments
@jenny_molloy World Health Organisation – 9 April 2015

2. What is content?

3. What is mining?
1982
“Automatically generating logical representations of
text passages... by means of an analysis of the
coherence structure of the passages.”
Jerry R. Hobbs, Donald E. Walker, and Robert A. Amsler. 1982. Natural language access to structured text. In Proceedings of the 9th
conference on Computational linguistics - Volume 1(COLING '82), Ján Horecký (Ed.), Vol. 1. Academia Praha, , Czechoslovakia, 127-132.
DOI=10.3115/991813.991833 http://dx.doi.org/10.3115/991813.991833
2008
“The use of automated methods for exploiting
the enormous amount of knowledge available in
the biomedical literature.”
Cohen, K. Bretonnel; Hunter, Lawrence (2008). "Getting Started in Text Mining". PLoS Computational
Biology 4 (1): e20. doi:10.1371/journal.pcbi.0040020. PMC 2217579.PMID 18225946.

4. Legal Considerations
Copyright
Database
rights
Contract
Law

5. 2011
2014
From 2014
UK Law
Workshops, hackdays, presentations, collaborations,
discussions with librarians and publishers.
Putting new rights into action.

6. In Europe
2013
Shortly after
20132015
Research commisioned through H2020...any EU Directive >5 years away.
Ireland already considering following UK - plus other member states?.

7. OUR MISSION
“make 100,000,000 facts
from the scholarly literature
open, accessible and reusable”

8. SOFTWARE OVERVIEW
quickscrape & thresher
norma
AMI fact extraction

9. THE SCALE OFTHETASK
• ~ 27,000 peer reviewed journals*
• > 5,000 publishers
• ~ 3,000 new papers per day
*Ulrich’s database: http://ulrichsweb.serialssolutions.com/login

10. STRUCTURED INFORMATION
• chemical names and structures
• species
• metabolism
• phylogenetic trees

11. SOFTWARE PIPELINE
PRODUCT:
PROCESS:
journals
(ISSNs)
fulltext
URLs
metadata +
content +
files
facts
crawl scrape extract

12. CRAWLING
The latest journal
tables of contents
at Journal TOCs
http://www.journaltocs.hw.ac.uk/

13. SCRAPERS
• all have the same plumbing
• scraping software (thresher) handles the plumbing
• scraperJSON is a config file
• supports large collections of scrapers
• no programming required
• not limited to one piece of software

14. BASIC SCRAPER JSON
name of the scraper:
the URL(s) it applies to:
the elements to capture:
element name:
where to find it:
{!
"name": "PLOS",!
"url": "plosw*.org",!
"elements": {!
"title": {!
"selector": “//h1[@property=‘dc:title’]”,!
}!
}!
}!
http://github.com/ContentMine/scraperJSON

15. SCRAPERS

16. SCRAPERS
{!
"name": "PLoS",!
"url": "plosw*.org",!
"elements": {!
"title": {!
"selector": “//h1[@property=‘dc:title’]”,!
}!
}!
}!

17. SCRAPERS
{!
"title": "Ab Initio Identification of Novel
Regulatory Elements in the Genome of Trypanosoma
brucei by Bayesian Inference on Sequence
Segmentation"!
}!
bibJSON output

18. THRESHER & QUICKSCRAPE
• reference implementation of scraperJSON
• thresher is the scraping library
• http://github.com/ContentMine/thresher
• quickscrape is the command-line tool
• http://github.com/ContentMine/quickscrape
• Node.js, MIT licensed

19. JOURNAL SCRAPERS
http://github.com/ContentMine/journal-scrapers
a self-testing collection of scraperJSON scrapers for academic journals
PLOS MDPI
PeerJ Wiley
ScienceDirect Taylor & Francis
NPG, AAAS, RSC, ACS Springer

20. NORMALISATION
quickscrape HTML
PDF
XML
DOC
CSV
Norma
sHTML
AMI
fact
extraction

21. NORMALISATION
before after
• un-navigable
• non-unicode
• pixel glyphs
• no structure
• processable
• sectioned
• tagged
• structured

22. NORMALISATION
mending on a journal-by-journal basis
invalid XHTML
from PLOS ONE
invalid XHTML
from BMC

23. NORMALISATION
document structure
before: un-sectioned
HTML from Hindawi
after: sectioned and
tagged HTML

24. FACT EXTRACTION
we can’t turn a
hamburger into a cow
but we can
turn PDFs
into science

25. FACT EXTRACTION
AMI software: https://bitbucket.org/petermr/ami-core
pixel
path
shape
char
word…
!
!
para
document
SCIENCE

26. FACT EXTRACTION
• titles
• scale
• units
• ticks
• quantity
• + data
DATA!!%
2000+%points%
VECTOR%%PDF%

27. FACT EXTRACTION
raw mobile photo
shadows, contrast,
noise, skew
binarization:
pixels = 0, 1
clipping
AMI-chem for extracting chemical formulae

28. FACT EXTRACTION
thinning chemical optical
character recognition
down to 1- pixel
AMI-chem for extracting chemical formulae

29. FACT EXTRACTION
thinning topology
AMI-phylo for extracting phylogenetic trees

30. FACT EXTRACTION
Newick format can be viewed at:
http://www.unc.edu/~bdmorris/treelib-js/demo.html
AMI-phylo for extracting phylogenetic trees
serialization
((n122,((n121,n205),((n39,(n84,((((n35,n98),n191),n22),n17))),((n10,n182),
((((n232,n76),n68),(n109,n30)),(n73,(n106,n58))))))),((((((n103,n86),
(n218,(n215,n157))),((n164,n143),((n190,((n108,n177),(n192,n220))),
((n233,n187),n41)))),((((n59,n184),((n134,n200),(n137,(n212,
((n92,n209),n29))))),(n88,(n102,n161))),((((n70,n140),(n18,n188)),(n49,
((n123,n132),(n219,n198)))),(((n37,(n65,n46)),(n135,(n11,
(n113,n142)))),(n210,((n69,(n216,n36)),(n231,n160))))))),(((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)))))));

31. Mining Examples
Building bacterial supertrees
Mining chemical reactions
Better genome annotation

32. Chemistry
AMI reads and recognises chemicals
structures.
Can even create reaction animation.
Natural language processing
can be used to analyse
chemical methods. These are
FACTS but the paper itself may
be copyrighted.

33. Clinical Trials
Clinical trials offer clear use cases
for content mining.
Data extraction from graphs could be very
useful for meta-analyses where raw data is
unavailable.

34. Only ~4% phylogenetic analyses
make underlying data available.
Supertrees
Content Mining enables AUTOMATED
extraction from daily literature and
conversion to NeXML:
- Machine-readable
- Open
- Reuseable
RAW data would be optimal!
PLUTo: Ross Mounce & Peter Murray-Rust

35. Annotation
Many applications:
- Find primers
- Enhance positive controls
- Find novel sequence information
- More detailed and accurate annotation
Potential to improve
quality and efficiency
of genomic research.

36. WHO
Thank you very much
for your attention!
Any questions?
Peter Murray-Rust
Ross Mounce
Richard Smith-Unna
Steph Unna
Jenny Molloy
Mark MacGillivray
Graham Steel
With thanks to:
Charles Oppenheim
Michelle Brook
Follow
@TheContentMine
contentmine.org
Find the code on
github.com/Content
Mine
Funded by:
Why might ContentMine be of interest?
Training for pubic health data researchers.
'Science on a Stick' standardised scholarly HTML
corpus for mining.
Potential to mine other standardised PDF documents
such as reports.
Open source, academic-led, easy to use and
customise.

37. All images are licensed under CC-BY unless otherwise stated
What is Content?
Phylogenetic Tree from Figure 1 in Evolution and Taxonomic Classification of Human Papillomavirus 16 (HPV16)-Related Variant Genomes: HPV31,
HPV33, HPV35, HPV52, HPV58 and HPV67. Chen Z, Schiffman M, Herrero R, DeSalle R, Anastos K, et al. (2011) Evolution and Taxonomic
Classification of Human Papillomavirus 16 (HPV16)-Related Variant Genomes: HPV31, HPV33, HPV35, HPV52, HPV58 and HPV67. PLoS ONE 6(5):
e20183. doi: 10.1371/journal.pone.0020183
Graph from He F, Fromion V, Westerhoff HV. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression
regulation: marrying control engineering with metabolic control analysis. BMC Syst Biol. 2013;7 131. doi:10.1186/1752-0509-7-131. PubMed PMID:
24261908; PubMed Central PMCID: PMC4222491.
Table from Table 1 Young GR, Mavrommatis B, Kassiotis G. Microarray analysis reveals global modulation of endogenous retroelement transcription by
microbes. Retrovirology. 2014;11 59. doi:10.1186/1742-4690-11-59. PubMed PMID: 25063042; PubMed Central PMCID: PMC4222864.
Text from Laidlaw CT, Condon JM, Belk MC. Viability Costs of Reproduction and Behavioral Compensation in Western Mosquitofish (Gambusia affinis).
PLoS One. 2014;9(11) e110524. doi:10.1371/journal.pone.0110524. PubMed PMID: 25365426; PubMed Central PMCID: PMC4217728.
Cell microscopy image from Pettinato G, Vanden Berg-Foels WS, Zhang N, Wen X. ROCK Inhibitor Is Not Required for Embryoid Body Formation from
Singularized Human Embryonic Stem Cells. PLoS One. 2014;9(11) e100742. doi:10.1371/journal.pone.0100742. PubMed PMID: 25365581; PubMed
Central PMCID: PMC4217711.
Supertrees:
Lang JM, Darling AE, Eisen JA. Phylogeny of bacterial and archaeal genomes using conserved genes: supertrees and supermatrices. PLoS One.
2013;8(4) e62510. doi:10.1371/journal.pone.0062510. PubMed PMID: 23638103; PubMed Central PMCID: PMC3636077.
McDowell A, Nagy I, Magyari M, Barnard E, Patrick S. The opportunistic pathogen Propionibacterium acnes: insights into typing, human disease, clonal
diversification and CAMP factor evolution. PLoS One. 2013;8(9) e70897. doi:10.1371/journal.pone.0070897. PubMed PMID: 24058439; PubMed Central
PMCID: PMC3772855.
Chemistry:
Diagram from Klejnstrup ML, Frandsen RJ, Holm DK, Nielsen MT, Mortensen UH, Larsen TO, Nielsen JB. Genetics of Polyketide Metabolism in
Aspergillus nidulans. Metabolites. 2012;2(1) 100-133. doi:10.3390/metabo2010100. PubMed PMID: 24957370; PubMed Central PMCID: PMC3901194.
Methods text from Greshock, T. J., Grubbs, A. W., Jiao, P., Wicklow, D. T., Gloer, J. B., & Williams, R. M. (2008). Isolation, Structure Elucidation, and
Biomimetic Total Synthesis of Versicolamide B, and the Isolation of Antipodal (−) Stephacidin A and (+) Notoamide B from Aspergillus versicolor NRRL‐ ‐
35600. Angewandte Chemie m frokInternational Edition, 47(19), 3573-3577.
Annotation:
Stubben, C. J., & Challacombe, J. F. (2014). Mining locus tags in PubMed Central to improve microbial gene annotation. BMC bioinformatics, 15(1), 43.
Figure from Haeussler, M., Gerner, M., & Bergman, C. M. (2011). Annotating genes and genomes with DNA sequences extracted from biomedical
articles. Bioinformatics, 27(7), 980-986.