The document discusses using automated workflow tools like KNIME and Semantic Web technologies to improve the accuracy of chemical and pharmaceutical data on Wikipedia. It describes generating a report of all chemicals and drugs on Wikipedia by extracting data from the Chembox and Drugbox templates, and then using SPARQL queries on Wikidata to compile the information more easily in a standard format. The goal is to allow subject matter experts to review and correct the data to link it unambiguously to the FDA's Substance Registration System.

1. Using Automated
Workflow Tools to
Improve Wikipedia
MITCH MILLER
SCIENTIFIC THINKING
VERMONT CODE CAMP 2016
SEPTEMBER 17, 2016

2. Disclaimer
 This talk represents my opinion and personal experience using software
systems developed by third parties
 The software systems shown are very complex and have hundreds of
components. I have only worked with a small number.
 Every task shown today can be accomplished in multiple ways. I’m only
showing some of those ways.

3. Overview
 Introduction: how are we improving Wikipedia? Why are we doing this?
 The list of information we need to compile
 First method of generating the list
 The second method of generating the list
 The third method of generating the list

4. What chemistry does Wikipedia
contain?
 9,736 articles with the Chembox; 5,656 with the Drug box (15, 392 total)
[source: https://en.wikipedia.org/wiki/Wikipedia:List_of_infoboxes]
 Chembox? Drug box?
 Templates of selected content within Wikipedia articles
 Contents of Chembox:
 Molecular structure image
 Name (systematically assigned name + synonyms)
 Identifiers: CASNo, ChEBI, ChEMBL, ChemSpiderID, DrugBank, InChI, KEGG,
PubChem, SMILES, UNII…
 Key properties

5. Chemical identifiers
 Different specific databases
 Individual IDs have strengths and weakness
 The UNII is a non- proprietary, free, unique, unambiguous, non semantic,
alphanumeric identifier based on a substance’s composition and/or
descriptive information.
 http://www.fda.gov/ForIndustry/DataStandards/SubstanceRegistrationSystem-
UniqueIngredientIdentifierUNII/
 UNIIs contain 9 randomly generated alphanumeric characters with a tenth
check alphanumeric character
 When two samples have the same UNII, “they represent the same molecular
entity or elements upon which the definition is based.”

6. SRS group goal
 Manages Substance Registration System (SRS)
 Assure uniformity of UNII assignments across internet resources that
reference UNIIs

7. The assignment
 Generate a report of all chemicals and drugs in Wikipedia
 Name, UNII (when present), CAS (when present),Wikipedia URL
 Idea: subject matter experts will review list and correct assignments, add
new UNIIs to Wikipedia as needed
 Result: more accurate Wikipedia that links to the FDA’s Substance
Registration System unambiguously
 https://fdasis.nlm.nih.gov/srs/srs.jsp

8. Development tool: KNIME
 Graphic, component based programming environment
 Drag functional components from palette onto canvas to create program
 Configure most components by setting parameters
 Connect components to route data from one to another
 Run and observe data traveling down the lines
 KNIME stands for KoNstanz Information MinEr
 Pronounced “Nighm”
 Originally a production of the University of Konstanz, Germany 2004
 Currently produced by KNIME.com AG, a company in Zurich, Switzerland
 Free version available for download
 Windows, Linux, Mac

9. First method of report generation
 Read list of pages with each infobox
 E.g.,
https://en.wikipedia.org/w/index.php?title=Special:WhatLinksHere/Template:Ch
embox&limit=50000&from=16225610&back=0
 Retrieve each individual page mentioned in the list
 Parse HTML
 Use Xpath to get Name, CAS, UNII
 The Infobox templates lead to pages with defined structure – straightforward to
parse
 Format data for output
 Write to a file

10. First method: pluses/minuses
 Plus: it works
 Minus: had to run in batches to get all records
 Minus: XPath parsing was more cumbersome than expected
 Minus: misses some data

11. The Semantic Web
 A connected set of data resources that can be understood by machines
 Data encoded in a standard way that allows unattended processors to
traverse links from one entity to another across organizational and
geographic boundaries
 [Standard WWW is a web of documents meant to be understood by
humans]
 Tim Berners-Lee has a great Ted talk on the semantic web
 https://www.youtube.com/watch?v=OM6XIICm_qo

12. Understand Semantic Web in
comparison to WWW
 Compare pages on same subject:
 Wikipedia article on ethanol: https://en.wikipedia.org/wiki/Ethanol
 Wikidata page on ethanol: https://www.wikidata.org/wiki/Q153

13. Technological foundations of Semantic
Web
 RDF – Resource Definition Framework – organizing facts as
 Subject – Predicate – Object
 Conceptual example:
 [Ethanol] [has a boiling point] [173 degrees Fahrenheit]
 Coded example:
 Wd:Q153 wdt:P2102 “173±1 degree Fahrenheit” .
 Represented in Turtle - Terse RDF Triple Language

14. SPARQL
 Query language for RDF data
 SPARQL Protocol and RDF Query Language
 Similar to SQL
 Syntax based on the RDF triple

15. Wikidata
 Conceptually: semantic web version of Wikipedia
 Add grain of salt
 “Free and open knowledge base that can be read and edited by both
humans and machines. “
 Designed as ‘central storage’ for Wikipedia and other Wikimedia projects
 Approximately: programmatic interface to Wikipedia
 See https://query.wikidata.org/
 Run the example queries

16. Second method
 Search Wikidata programmatically for chemical information
 Wikidata SPARQL interface
 Format list
 Write file

17. SPARQL for chemical and
pharmaceutical compounds
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wikibase: <http://wikiba.se/ontology#>
PREFIX bd: <http://www.bigdata.com/rdf#>
#All Chemicals with, optionally, CAS registry numbers and UNIIs in Wikidata
SELECT DISTINCT ?compound ?compoundLabel ?formula ?unii ?pubchem ?cas WHERE {
?compound wdt:P31 wd:Q11173 .
OPTIONAL { ?compound wdt:P231 ?cas . }
OPTIONAL { ?compound wdt:P274 ?formula . }
OPTIONAL { ?compound wdt:P652 ?unii . }
OPTIONAL { ?compound wdt:P662 ?pubchem . }
SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}

18. Second method: pluses/minus
 Fast and easy!
 Data arrives in a format we can use – no parsing!
 Minus:
 *some* Wikidata data does not match up with Wikipedia!

19. Third method
 Hybrid approach
 Use Wikidata SPARQL query to get list of chemicals
 Query Wikipedia for individual items to compare values

20. Conclusion
 Using Wikidata, Wikipedia and KNIME we compiled a list of chemicals with
the required data
 Subject matter experts are in the process of updating Wikipedia
 Semantic web technology made the job easier!
 Thank you!

21. References
 Scholarly article on KNIME and Pipeline Pilot
 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3414708/
 KNIME
 www.knime.org
 Wikipedia
 https://en.wikipedia.org/wiki/Template:Chembox
 https://en.wikipedia.org/wiki/Wikipedia:Chemical_infobox
 Wikidata: https://query.wikidata.org

22. Who is your speaker?
 Mitch Miller, Ph.D. in Chemistry and 20+ years of IT experience
 Independent consultant: Scientific Thinking, LLC
 mitch.miller@thinkscience.us
 Some recent projects
 Ongoing custodian of one chemical database implementation for ChemIDplus
project within the National Library of Medicine
 Reporting systems
 Web service to link collaborative object management system to reporting
system
 Import wizard for chemical array designer
 Merged a set of chemical databases and harmonized data