Value adds of, reproducibility challenges, use cases, practical recommendations, success actors, key take aways

1. You thought taxonomies were hard?
How to create a taxonomy
for “management buy-in"
Mary Chitty MSLS
mchitty@healthtech.com
https://www.cambridgeinnovationinstitute.com/
http://www.healthtech.com/
1
London, Oct 15, 2019
Cambridge
Healthtech
Needham MA USA

2. Where I’m coming from
1992
2000
2006-
2014
2016
2018
2019 Taxonomy now 1,600+ terms and
growing. Company is now mid-size.
2018 Signed contract with OntoForce
to use Disqover search software.
https://www.ontoforce.com/
Acquired Artificial Intelligence
and Internet of Things
companies. Hired several data
scientists.
Began informal
collaboration with
OntoForce, Belgian
semantic search
engine company.

3. Taxonomies &
Ontologies:
Many useful
applications
• E-commerce
• Data visualization
• Search, Semantic search and Search Engine
Optimization SEO
• Statistical analysis
• Text mining

4. Ontologies
are complex
Simpler is
sometimes
better
• “Ontologies offer advantages over other knowledge
systems—they enable both computational use and
human understanding, they can …include rich
vocabularies of labels, synonyms, and textual
definitions. If these are desirable selection criteria, then
an ontology should be considered. ”
• “Ontologies do also come with computational
overheads, however, and can be complex to understand.
Other resources such as a vocabulary do not offer the
sorts of classification and rich computational
descriptions of an ontology but are often much simpler
to understand. Let your requirements guide you;
ontologies are not a panacea—sometimes
one isn’t needed at all. ”
• Malone 2016

5. Value adds of taxonomies/ontologies:
Interoperability
Reproducibility
Data harmonization especially of named entities
Identification of data errors or inconsistencies
Search/data navigation improvements
Collaborative filtering/recommendation engines
Validation of correlations to examine possible causalities
Enabling previously unaskable questions/use cases

6. Information overload
Complexity
Data integration
Legacy data
Ambiguous and inconsistent data
Missing or unfindable data
Scaling up data processes
Sustainable maintenance – perhaps the biggest challenge of all!
Ongoing Challenges:

7. Primary challenges are as much cultural as technological
Life sciences challenges include:
Relatively sparse data compared to other domains such as financial
Highly dimensional data with many variables (complex to chaotic)
Inherently noisy biological data. (Increasingly studied at the single cell or
gene expression level).
Data on longitudinal health outcomes limited by HIPAA & other privacy
regulations, but crucial for evidence based medicine validation.
In our era of big data, the irony is we don’t have enough readily usable life
sciences data.

8. Reproduci
bility
challenges
• More than 70% of researchers have tried but
failed to reproduce experiments. More than
half failed to reproduce their own
experiments. Baker, Nature 2016
• “replication alone will get us only so far (and)
might actually make matters worse… an
essential protection against flawed ideas … is
the strategic use of multiple approaches to
address one question. Each approach has its
own unrelated assumptions, strengths and
weaknesses. Results that agree across different
methodologies are less likely to be artefacts”.
Wikipedia “replication crisis”

9. Cost benefit
Return On
Investment
ROI
Educating decision makers is an ongoing process, even
with CXOs who value taxonomies/ontologies.
But stakeholders are often skeptical about investing in
taxonomies or ontologies.
Minimum of 10.2 billion Euros per year.
Cost of not having FAIR research data:

10. Semantic
web people
have talked
about
terminology
challenges
for decades
2001
"The first layer of the semantic Web consists of ontologies and
taxonomies ... A huge amount of this is being done very desperately
in the realm of biotech, for the human genome and new drug
development.”
Tim Berners Lee, August 30, 2001 keynote at Software
Development East in Boston.
2005
“Semantics is fundamentally not an information technologies issue
…it originates out of the need for groups of individuals to work
together towards common goals … must agree upon a set of
meanings around terminologies, concepts, relations and actions …
a lot of confusion arises before people realise whether they are
talking about the same or different things."
Eric Neumann, Applying the semantic web to drug discovery and
development. Drug Discovery World Fall 2005

11. Business
cases for
taxonomies
or
ontologies
is not a new
problem
“a month in the lab can often save an hour in the
library” Attributed to chemist Frank Westheimer
1979 interview
“Institutions either underestimate the resources
needed to do this work , or they are daunted by
the entire prospect ... Honestly, very little data
will ever be reused. ” personal communication,
Juliane Schneider, eagle-i, Harvard Catalyst

12. Best Practical Advice I’ve come across
“One of my mantras is always start small. Show some win in some
small domain. Don’t under any circumstance start with saying I’ll just build you this
enormous ontology for the next two years …then your world will be better”. .. Just
say I’m going to build this tiny little ontology and enable this small application over here
… Always making sure my small ontology is enabling the small win.”
“Question: how do you encourage semantic modeling? Answer: First I compliment them,
and say what you’ve done is a great starting point – because they have actually started …
I try to find a couple of structured retrieval applications that they really want to do but
their current markup is not allowing ….find two compelling examples … make sure
that we’ve got a deliverable in a month or a short period of time where they can do the
one trial thing that adds value. Kind of get them on the slippery slope so that they’re’ the
owner and they want to do it themselves.” Deborah McGuinness keynote speech 2004

13. Taxonomy Use Cases
Amazon I spoke at Taxonomy Boot Camp
2017 in Washington DC and learned that
Amazon has taxonomists on 24 hour
emergency call, for when people can’t find
their products online.
Netflix “[Netflix]paid people to watch films
and tag them with all kinds of metadata.
This process is so sophisticated and precise
that taggers receive a 36-page training
document that teaches them how to rate
movies ...
“[Netflix] even offered a $1 million prize to the team that could design
an algorithm that would improve the company's ability to predict how
many stars users would give movies. It took years to improve the algorithm
by a mere 10 percent. The prize was awarded in 2009, but Netflix never
actually incorporated the new models. ” Madrigal, Atlantic 2014
Best business case for
taxonomy ever?

14. My Taxonomy Case Studies in-house
database
Map industry verticals to
company specific verticals
Question: Can we use any
existing taxonomies such as
NAICs codes and CrunchBase
to automate? How to
integrate existing in-house
database with newly
acquired company
databases? Work in
progress.
Job title functions and
seniorities for people in
database
Data scientist automated. At
least 80% assigned now.
Customizable for use by
various departments?
Phase 1 just completed. Still
reviewing and fine-tuning.
Job departments for people
in database.
Similar to job titles. Phase 1
just started.
Ontoforce internal data
ingestion
Project to enable improved
access to existing in-house
database. Uncovered
inconsistencies with labels
and tables. Identified data
quality issues to address.
Working on training users,
documenting workflows.
Need to add more
changeability to existing
taxonomy keywords
structure. Starting to see how
trend analysis may be
possible. Work in progress.

15. My
recommendations
for starting a
project
• Consider a pilot/proof of concept.
• Start small because that will be easier to evaluate
and validate.
Don’t try to “boil the ocean”.
• Choose a variety of data complexity. Think about
degrees of granularity when drafting categories.
• Which categories might you want to aggregate?
• Which related concepts might you want to segment
further? [Phase 2]
• Are there assumptions or implicit biases you might
be making without realizing?
• Solicit feedback from diverse stakeholders as an
ongoing process.

16. Don’t be surprised while building your pilot project :
Terminology consensus is challenging at best.
Taxonomies, ontologies,
naming, tagging, tables,
models
Many ways to express
these concepts.
“Biologists would rather
share a toothbrush than
gene names” Michael
Ashburner GeneSeer: A
sage for gene names and
genomic resources
"Biologists would rather
share a tooth brush than
data” Carol Goble
“purposely misquoting
Michael
Ashburner” Keynote EGEE
2006
Trying for consensus often
gets very emotional,
challenging – and
confusing.

17. https://www.go-fair.org/fair-principles/
Findable
•First step in
(re)using data is to
find them.
Metadata and data
should be easy to
find for
both humans and
computers. … an
essential
component of
the FAIRification
process.
Accessible
•Once the user
finds the required
data, she/he
needs to know
how can they be
accessed
Interoperable
•Data usually need
to be integrated
with other data …
need to
interoperate with
applications or
workflows.
Reusable
•Ultimate goal of
FAIR is to optimise
the reuse of
data… metadata
and data should
be well-described
so that they can be
replicated and/or
combined in
different settings.
Keep in mind for management presentation
FAIR data can help
European Commission and US National Institute of Health have allocated considerable resources to making data FAIRer.

18. Managemen
t may ask
“Why can’t I
just Google
it?”
• Search works best if you know what you’re looking
for exists AND what to call it.
• Taxonomies are more useful if you’re not sure
what you want exists, OR you don’t know what to
call it, AND/OR there are multiple ways to express
concept variations.
• Harness the power of serendipity with
taxonomies. They give people a sense of whether
the “scent of information” is promising.

19. Success
Factors
Business
case
Manageme
nt:
Look for and document
Cost reductions
Productivity increases
Employee time savings
Added competitive advantages
Sustainability
Risk mitigations
Assemble
Sponsors, champions and/or influencers
Clear executive summary, with KPIs Key Performance
Indicators, milestones, values, costs
1-2 pages Utilize url links if needed
Remember
Align early. Align often.
Ask for feedback – it’s a way of getting buy-in.
Leave room for suggestions.
Be aware of other company initiatives.

20. But what else needs to
change?
• Data-readiness
“[T]here is a lot of work that needs doing to prepare the data sets for these technologies
… a disproportionate amount being invested in the technologies as opposed to investing
in "data-readiness… It's just not a slam dunk to mash up a lot of data and think it
will work. … The AI solution may help accelerate some tasks, but human expertise may
be required for the broad scope of what is needed. “ Nicholas 2019
• Open Science
"Is any lifetime long enough these days to learn everything needed to get a drug to
market and keep it there? "
• More need than ever to collaborate to share knowledge, especially pre-competitively.

21. Key takeaways
1. Aim first for quick wins with low hanging fruit.
2. Bundle stakeholders valued wants with items you can expect they will
eventually need.
3. Seek out allies to get shared buy-in for sustainable justification.
4. Pareto Principle 80% of effects come from 20% of effort.
5. Expectations/change management are crucial skills to cultivate.
6. Collect metrics (quantitative/qualitative) to measure progress, so you know
when you’ve made some.
7. Recognize some challenges haven’t been resolved by anyone yet.

22. Resources to use todayChitty, Mary, Ontologies & Taxonomies glossary & taxonomy, 2019 with 40 plus ontology definitions, 15 taxonomy definitions http://www.genomicglossaries.com/content/ontologies.asp
Heath, Chip and Dan, Switch: How to Change Things When Change is Hard, 2010 https://heathbrothers.com/books/switch/
McGuinness, Deborah, Ontology Development 101, A Guide to creating your first ontology http://www.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness-abstract.html
Research Data Alliance https://www.rd-alliance.org/ a research community organization started in 2013 by the European Commission, US National Science Foundation, US National Institute of Standards and
Technology, Australian Department of Innovation.
Citation References
How Netflix Reverse-Engineered Hollywood, Atlantic, 2014 https://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/
Life Science specific
BioPortal https://bioportal.bioontology.org/ repository of biomedical ontologies has almost 800 ontologies, mapping from ontologies to I2B2 http://i2b2.bioontology.org/
Malone, James et. al. Ten Simple Rules for selecting a Bio-Ontology, PLoS Comput Biol 12(2), 2016: e1004743. https://doi.org/10.1371/journal.pcbi.1004743
National Center for Biomedical Ontologies NCBO BioPortal Ontology to i2b2 File Mappings http://i2b2.bioontology.org/
Pistoia Alliance, Ontologies Guidelines for Best Practices to support practical application and mapping, 2016 https://pistoiaalliance.atlassian.net/wiki/spaces/PUB/pages/43089942/Ontologies+Guidelines+for+Best+Practice
Berneres Lee 2001 http://www.sdgnews.com/sd2001es_006/sd2001es_006.htm no longer on web
Neumann, 2005 https://www.ddw-online.com/informatics/p148329-applying-the-semantic-web-to-drug-discovery-and-development.html
Michael Ashburner GeneSeer: A sage for gene names and genomic resourcesBMC Genomics. 2005; 6: 134. 2005 Sep 21. doi: 10.1186/1471-2164-6-134
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1266031/
Carol Goble “purposely misquoting Michael Ashburner” Keynote EGEE 2006
Interoperability With Moby 1.0 - It's Better Than Sharing Your ...
Semantic alignment and data standardization are vital to solve if we are going to harness modern technologies such as machine learning”
Ian Harrow1 Rama Balakrishnan2 Ernesto Jimenez-Ruiz34 Simon Jupp5 Jane Lomax6 Jane Reed7 Martin Romacker8 Christian Senger9 Andrea Splendiani10 Jabe Wilson11 Peter Woollard12 Drug Discovery Today May 2019 https://www.sciencedirect.com/science/article/pii/S1359644618304215
Nicolas 2019 Life Science Leader 2019 March 1, “AI In Life Sciences: Seeing past the Hype” Francois Nicolas and comment by Christy Wilson https://www.lifescienceleader.com/doc/ai-in-life-sciences-seeing-past-the-hype-0001

23. Acknowledgments
Many people have participated in this ongoing project. I’m grateful for their work, insights and
encouragement.
Cambridge Innovation
Institute CII
& Cambridge Healthtech
Institute CHI
•Phillips Kuhl, President
•Tonya Urquizo,
Knowledge Information
Services Analyst & IT
Liaison
Sanaye Bartlett, Data
Analyst & Project Manager
•Kaushik Chaudhuri,
Director of Product
Marketing
CII Disqover Team
•Kaitlyn Barago, Associate
Conference Producer
•Nancy Clarke, Data
Scientist
•Mike Croft,
Software Architect
•Ben Lakin,
Director New Initiatives
•Jaime Parlee, Director
Marketing Analytics
•Craig Wohlers, Manager
Knowledge Foundation
OntoForce
•Hans Constandt, CEO
President
•Filip Pattyn, Scientific Lead
•Niels Vanneste,
Customer Data Scientist
•Berenice Wulbrecht, Data
Science Director, Systems
Biology
•Fruitful conversations
Emails
•Ingrid Akerblom, IFA
Diversified Consulting
John Aubrey, Vertex
Mark Burfoot, Novartis
NIBR
Jane Lomax, SciBite
•Eric Neumann, Akidata LLC
•Terrell Russell,
iRODS Consortium
•Juliane Schneider, eagle-i,
Harvard Catalyst
•Ted Slater, PaaS, Elsevier
•John Wilbanks,
Sage Bionetworks