This presentation was given at the CLIR/DLF Postdoctoral Fellowship Summer Seminar at Bryn Mawr college in Pennsylvania on July 29th 2014. The intention was to communicate what we are doing in the fields of text and data mining in the domain of chemistry and specifically around mining the RSC archive publication and chemistry dissertations and theses. How would these experiences map over to the humanities?

1. Data mining dissertations
Adventures and Experiences in the World of Chemistry
Antony Williams
CLIR/DLF Postdoctoral Fellowship Summer Seminar,
July 2014

2. What a small world…

3. • Who’s got an ORCID?
• Who has heard of/involved with AltMetrics?
• Who has edited a Wikipedia page?
• Who has direct experience of text mining?
• All slides already on Slideshare here:
• www.slideshare.net/AntonyWilliams
Before we start….

4. • Context – why do we want to mine data?
• Our experiences in extracting theses:
– Text and data mining
– Chemistry as an example
– Before you start
– Resources and tools
Contents

5. • Let’s map together all historical chemistry
data and build systems to integrate
• Heck, let’s integrate chemistry and biology
data and add in disease data too
• Let’s model the data and see if we can
extract new relationships – quantitative and
qualitative
• Let’s make it all available on the web
Taking on a big challenge…

7. • We’re going to map the world
• We’re going to take photos of as many
places as we can and link them together
• We’ll let people annotate and curate the map
• Then let’s make it available free on the web
• We’ll make it available for decision making
• Put it on Mobile Devices, Give it Away
What about this….

8. I’m from here…on Google

9. Wikipedia

10. Wikipedia

14. The Power of Contribution

15. How do you spell Afonwen?

16. And there’s Denbigh…

17. • So the world can be mapped…
• We can enter a 3D world within the map
• We can add annotations
• We can use the data, reference it, we can
extract it, we can make decisions with it
• And we can do it on our lap, in our hands
• Let’s do this for chemistry…
Whoa…

18. • Once upon a time we built a database….
In a basement not far away…

19. ChemSpider

20. ChemSpider and Data Validation

21. Dictionary Linking

22. Dictionary Linking

23. • This is not new, you known the story…
• So much data of value contained within a
publication and delivered in a PDF form
• “PDF files, and especially unclear licensing,
don’t allow me at the data so I can rework,
reuse, repurpose, text mine etc.”
• “I specialize in XXXX. I want a database of
YYYY extracted from publications and made
available, for free, with capabilities I need,
and the publishers should just do it”
Data in a Scientific Publication

24. It is so difficult to navigate…
What’s the
structure?
Are they in
our file?
What’s
similar?
What’s the
target?Pharmacology
data?
Known
Pathways?
Working On
Now?Connections
to disease?
Expressed in
right cell type?
Competitors?
IP?

25. • Manage “all” of the chemistry data associated
with chemical substances
• Data to be downloadable, reusable, interactive
• Build a platform that enables the scientist
• Data storage, validation, standardization and
curation
• Collaborative data sharing
• Provide data platform that can enable and
enhance publishing of scientific papers
We set a vision…

26. • Every compound from every article at RSC is
extracted, in a database, and linked
• Chemical properties are extracted, databased
and used for predictive models
• Data tables are downloadable, interactive and
not just “dumb-PDFs”
• …and what can we extract from chemistry
theses too?
XXX Years from Now at RSC

27. • We are seen as one of the repositories for
published AND unpublished research data
• An intuitive platform for research data
management in the cloud
• Individual, collaborative and public data
management of diverse data in the cloud
• …and where all data referenced in a
thesis is available at a button click
XXX Years from Now at RSC

28. • But how does it map onto your domain??
So this is chemistry…

29. Mining as an allegory

30. • You have a mountain of stuff which
contains valuable nuggets
• You (more or less) know what you’re
looking for
• You know what you’re going to do with it
once you have it
Mining as an allegory - intent

31. • You get lots of stuff out
• It requires sifting and grading
• It’s a triumph if you manage to extract
80-90% of what is there
• You will go back to the heap and redo it
Mining as an allegory - result

32. • That which is easy to get out - is well
known and unlikely to be novel
• The novel and interesting stuff is likely to
be rare and not easily defined
Mining as an allegory - effort

33. • Do the initial investigations by hand
• Send in the machines later
• Still needs some humans tweaking
Mining as allegory - automation

34. Context

35. • From Utopia Documents team
• Good at extracting structure from typeset pdfs
• http://pdfx.cs.man.ac.uk/
PDFX

36. OCR recognition
• Underlining doesn’t help OCR
• In this case it was the only signpost to the
department, supervisor and funding details

37. • Hardcopy
• Scanned and OCR’d PDF
• PDF derived from Word
• Word or LaTeX
• …and for OCR not all are borne equal
• …and of course history and language is
a major influence. “Oil of vitriol”
Building blocks to mine…

38. • Ontologies, taxonomies, dictionaries
• But these are very domain focussed…
• As an example, Open PHACTS spend a
lot of effort mapping biology to chemistry
to disease over many data sources
More building blocks

40. • Provide a controlled vocabulary – what
your data describes, where it came from
• Provide a shared vocabulary for
integrating with other people’s data
What can ontologies do for me?

41. Questions to ask:
(1)Has someone already produced an ontology
covering your area? (Places to look:
Bioportal, OBO Foundry.)
(2)Do they take requests?
(3)Are they responsive?
(4)Is the ontology kept up to date?
Early days for ontologies and any ontology will
almost certainly be a long way from complete!
Best practices: experiences
from biomedical ontologies

42. • Best that these don’t change
• Best that everyone calls them the same things
• Best that they are unambiguous
• Meanwhile, back in the real world
What things are you looking for?

43. • Place names – somewhat ambiguous
• Species names – can change with time
• Diseases – every pharmaceutical company
has a different list
• People – can be very ambiguous: Authors
and researchers are hard to map…except
for Google it seems!
How easy?

44. http://www.amazon.com/-/e/B004YRPRV2

45. http://orcid.org/0000-0002-2668-4821

46. Thankfully people follow…

47. Google Scholar Citations
http://scholar.google.com/citations?user=O2L8nh4AAAAJ

48. ORCID take up???

49. • All publications easily connected but also
– Important in early scientific career –
consider every data point contribution, every
“research object”
– Every article
– Every presentation
– Thesis and dissertation
– Provenance….and feeding AltMetrics
So the benefits of ORCIDs?

50. The Alt-Metrics Manifesto

52. AltMetrics via Plum Analytics

53. Usage, Citations, Social Media

54. Detailed Usage Statistics

55. Indexed and Searchable

56. ORCIDS for reputation…

57. Tinman - mutant fly embryos lack a heart.
Van Gogh - hair-like bristles on wings have a swirling pattern.
INDY - acronym for I'm Not Dead Yet, they live twice as long as normal; from the scene in
the movie "Monty Python and the Holy Grail"
Ken and Barbie - males and females lack external genitalia.
Tribbles - some cells divide uncontrollably
Cheap date - flies are extra-sensitive to alcohol.
Cleopatra - flies die when Cleopatra gene interacts with another gene, Asp.
Kojak - no hairs on wings.
Maggie - fly development is arrested; named after Maggie Simpson, who's development
also seems to be arrested.
Oh my..Fruitfly gene names
• http://stlists.blogspot.co.uk/2005/05/fruitfly-gene-names.html

59. • those that belong to the Emperor,
• embalmed ones,
• those that are trained,
• suckling pigs,
• mermaids,
• fabulous ones,
• stray dogs,
• those included in the present classification,
• those that tremble as if they were mad,
• innumerable ones,
• those drawn with a very fine camelhair brush,
• others,
• those that have just broken a flower vase,
• those that from a long way off look like flies.
Allegedly from “Celestial Emporium of Benevolent Knowledge”
The Analytical Language of John Wilkins, Jorge Luis Borges
Animal classification

60. • Are you just identifying entities?
• Are you looking for sentiment?
• In chemistry names will lead you to a
recipe for synthesis, and analytical data
about that compound
Classification after “things”

61. • Used to aid discovery - directly
• Used to aid discovery - indirectly
• Extract data in electronic form for reuse
• Needs to be use case driven – why, then
what/how comes later
End result

62. • Automation can give good results
• Especially looked at in bulk
• Less easy to judge at the article level
• People accept discovery is fuzzy
• Not so with data points
• (but maybe can screen out)
Quality

63. • Chemical names are both difficult and rewarding.
• Difficult in the sense that they can break
standard software.
• Rewarding in the sense that you can extract
useful information about the molecule they’re
referring to without a dictionary.
• Some examples…
Chemistry-specific challenges
and opportunities

69. • …and it gets worse

70. A series of mono and di-N-2,3-epoxypropyl N-
phenylhydrazones have been prepared on a large scale
by reaction of the corresponding N-phenylhydrazones of
9-ethyl-3-carbazolecarbaldehyde, 9-ethyl-3,6-
carbazoledicarbaldehyde, 4-dimethyl-amino-, 4-
diethylamino-, 4-benzylethylamino-, 4-(diphenylamino)-,
4-(4,4-4′-dimethyl-diphenylamino)-, 4-(4-
formyldiphenylamino)- and 4-(4-formyl-4′-
methyldiphenyl-amino)benzaldehyde with
epichlorohydrin in the presence of KOH and anhydrous
Na(2)SO(4).
From Molecules, via the BioNLP list
Annotate this...

71. How many explicit compounds?
• How many numbered compounds
actually are named in a given
paper?
• iloprost (1)
• tributyl-1-hexynylstannane (2)
• the desired 2-heptyne (3)
• methyl–Pd(II) iodide 4 or 4′
• alkynylstannane 5
• the hypervalent stannate 6
• (alkynyl)(methyl)Pd(II) complex 7
• the desired methylalkyne 8
• compounds 9–14
• the stannyl precursors 15 and 16
• methylated compounds 17 and 18
• stannyl precursor 19
• iloprost methyl ester 20
• “iloprost methyl ester” is the real
name, but you need to know that
iloprost is a monocarboxylic acid!

73. Names from structures
• Systematic names can be generated FROM
chemical structures algorithmically

74. General-purpose parsers do
NOT get chemical names
Visualization by bpodgursky.com using d3.js; parsing by
Stanford’s CoreNLP.

75. But names can reverse back
to structures…

76. • OPSIN (chemical name to structure)
http://opsin.ch.cam.ac.uk/
Tools to try

77. Not all names are systematic..
Antony Williams vs Identifiers
Passport ID
Dad, Tony, others
SSN
Green Card
License
5 email addresses
ChemSpiderman (blog,
Twitter account,
Facebook, Friendfeed)
OpenID
….

78. Many Names, One Structure

79. Aspirin on ChemSpider

80. Unique Structure Identifiers

81. Structure Searching the Web

82. Certainly happens with Welsh!

83. • All of the tasks below are possible to varying extents.
Pioneered on journal abstracts and journal full text.
– Named entity recognition: what is this about? Where are
the places mentioned? Who are the people?
– Clustering and classification: which other dissertations
are like this one? What genres of dissertations are there?
– Event extraction: what processes (chemical reactions,
gene expression) occur? What are the participants?
– Citation analysis: who do dissertations cite?
– What sentiments towards the citations do authors express?
Dissertation analysis

84. • Dissertation copyright varies
• Institution
• Author
• Published or not?
Copyright issues

85. • Probably less structured than papers
• Not much work has been done here before
Dissertation specifics

86. • For example:
• Stylometrics (to find out who wrote this)
• Language identification
• What else?....
• In addition to above, there are different tasks
we can perform on scientific publications and
dissertations
Digital Humanities textual
analysis tasks

87. • We would LOVE to bring data out of our
archive
• What could we do?
• Find chemical names and generate structures
• Find chemical images and generate structures
• Find reactions – and make a database!
• Find data (MP, BP, LogP) and host. Build
models!
• Find figures and database them
• Find spectra (and link to structures)
• Validate the data algorithmically
“Data enable” publications?

88. RSC Archive – since 1841

89. Text Mining
The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-
thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride
( 5 ml ) and benzene ( 50 ml ) were charged into a glass
reaction vessel equipped with a mechanical stirrer ,
thermometer and reflux condenser .
The reaction mixture was heated at reflux with stirring , for a
period of about one-half hour .
After this time the benzene and unreacted thionyl chloride
were stripped from the reaction mixture under reduced
pressure to yield the desired product N-(β-chloroethyl)-N-
methyl-N'-(2-trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a
solid residue

90. Text Mining
The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-
thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride
( 5 ml ) and benzene ( 50 ml ) were charged into a glass
reaction vessel equipped with a mechanical stirrer ,
thermometer and reflux condenser .
The reaction mixture was heated at reflux with stirring , for a
period of about one-half hour .
After this time the benzene and unreacted thionyl chloride
were stripped from the reaction mixture under reduced
pressure to yield the desired product N-(β-chloroethyl)-N-
methyl-N'-(2-trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a
solid residue

91. • 13C NMR (CDCl3, 100 MHz): δ = 14.12
(CH3), 30.11 (CH, benzylic methane),
30.77 (CH, benzylic methane), 66.12
(CH2), 68.49 (CH2), 117.72, 118.19,
120.29, 122.67, 123.37, 125.69, 125.84,
129.03, 130.00, 130.53 (ArCH), 99.42,
123.60, 134.69, 139.23, 147.21, 147.61,
149.41, 152.62, 154.88 (ArC)
Text spectra?

92. 1H NMR (CDCl3, 400 MHz): δ = 2.57 (m, 4H, Me,
C(5a)H), 4.24 (d, 1H, J = 4.8 Hz, C(11b)H), 4.35 (t, 1H,
Jb = 10.8 Hz, C(6)H), 4.47 (m, 2H, C(5)H), 4.57 (dd,
1H, J = 2.8 Hz, C(6)H), 6.95 (d, 1H, J = 8.4 Hz, ArH),
7.18–7.94 (m, 11H, ArH)

93. Turn “Figures” Into Data

94. Make it interactive

95. SO MANY reactions!

96. Reactions From Patents

97. Experimental data checker

98. • http://chemicaltagger.ch.cam.ac.uk/
Tools to try: ChemicalTagger

99. Tools to try: ChemicalTagger

100. • ChemicalTagger
Tools to try

101. How is DERA going?
• We have text-mined all 21st century articles…
>100k articles from 2000-2013
• Marked up with XML and published onto the
HTML forms of the articles
• Required multiple iterations based on
dictionaries, markup, text mining iterations
• New visualization tools in development – not
just chemical names. Add chemical and
biomedical terms markup also!

102. Work in Progress

103. Work in Progress

104. Work in Progress

105. Work in Progress

106. Dictionary
(ontologies)
RSC ontologies
(methods,
reactions)
Dictionary
(chemistry)
Text-mining
Curated dictionaries for known names
ACD N2S
OPSIN
Unknown names: automated
name to structure conversion
XML ready for
publication
Marked-up
XML
Production
processes
CDX integration
(coming soon)
Chemical
structures SD
file
Is It Easy?

107. Our Supporting Ontologies

108. • The ‘National Compound Collection’
• Extracting compounds manually from theses
• 700 theses, 44,000 compounds (growing…)
• 4 months, 12 UK institutions
• Deposited into ChemSpider
A pilot examining theses

109. • Screening for interesting drug candidates
• Mapping the chain from author to institution
to data to industry
• British Library involved (EThOS collection)
• Build a business model for this
Pilot objectives

110. • Funders encouraging submission from new
dissertations
• Mining of old collections (mostly automated,
likely to need manual QA)
• Extension to other areas of chemical science
…and future (ideal)

111. • Don’t reinvent the wheel
• Research your domain to find work already
underway and test tools for value/utility
In your domain???
Most Domains are Active

112. A good place to start

113. • NaCTeM tools for e.g sentiment analysis
http://www.nactem.ac.uk/opminpackage/opinion_analysis
Tools to try

114. • NaCTeM tools for e.g sentiment analysis
Tools to try

115. There is always something new

116. Email: williamsa@rsc.org
ORCID: 0000-0002-2668-4821
Twitter: @ChemConnector
Personal Blog: www.chemconnector.com
SLIDES: www.slideshare.net/AntonyWilliams
Thank you