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Are Ontologies Relevant In A
Machine Learning World?
Dr. Lee Harland
Founder & Chief Scientific Officer, SciBite Limited
A...
The good news is
I have discovered
inefficiencies…
…The bad news is
that you are one
of them.
https://timoelliott.com/blog...
Ontologies Enable Us To Communicate
Knowledge Graph
Ontology
Taxonomy
Categorisation
Thesaurus
Controlled Vocabulary
List
...
What Do We Know About “Viagra”?
https://bioportal.bioontology.org
https://www.ebi.ac.uk/ols
Ontologies Enable
Machine Learning/A.I
Ontologies To Interpret ML Output Well Established
+100s more use ontologies to
interpret output from ML genomics
analysis
Ontologies Enhancing Training & Execution
… Clinically-driven
taxonomy of disease…
useful in generating
training classes t...
Enhancing Training & Execution (2)
doi:10.1038/s41591-018-0335-9
https://www.nature.com/articles/s41591-018-0335-9
https:/...
What’s Good For One….
…The overarching principles in
DeepQA are massive
parallelism, many experts,
pervasive confidence
es...
Validation *Ahem*
burnsburns burns
https://www.scibite.co
m/news/of-burns-and-
bums-machine-
learning-surprises/
© 2018 SciBite Limited
Building Ontologies With Machine Learning
Virtuous Circle Of Machine & Human Learning
Training
Execution
Interpretation
Enrichment
Validation
• Large numbers of disorganised documents (i.e. CRO documents)
• Need to align these to internal taxonomy of categories (e...
LifeArc Horizon Scanning
A Broader Viewpoint
HELP!
Ontologies Are The Key To Unlocking FAIR
….Therefore, we are
confident that the
true cost of not
having FAIR research
data...
SciBite Kusp – Making #cleandata easy
F.A.I.R @ AstraZeneca
https://www.slideshare.net/n1ck_brown/search-at-astrazeneca-an-agile-appstore-searchbased-apps-creat...
F.A.I.R @ BMS
• Public Bioassay
Ontology
• Augmented with
BMS-specific
terms
• Users can suggest
new assays etc.
• Reactiv...
SciBite Semantics Empower Scientific Infrastructure
F.A.I.R
Data
Catalogues
Knowledge
Graphs
Analytics
& ML/A.I
Semantic
Q...
Data’s Dynamic Duo
Acknowledgements
BMS: AIMS Team
AZ: R&D Search Team, Integrative Informatics Team,
Sinequa
Pfizer: Comp...
Thanks!
Slides @ https://www.slideshare.net/scibitely
Visit Us At http://scibite.com
IC-SDV 2019: Are Ontologies relevant in a Machine Learning World? - Lee Harland (CSO and Founder SciBite, UK)
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IC-SDV 2019: Are Ontologies relevant in a Machine Learning World? - Lee Harland (CSO and Founder SciBite, UK)

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The unescapable rise of machine learning (ML) and artificial intelligence (AI) challenges the role of existing text analytics techniques such as Named Entity Recognition and Natural Language Processing in extracting information from scientific text. Often these rely on underlying ontologies to provide the semantic foundation for more complex linguistic and statistical analysis. This paper investigates how ontologies and ontology-led text analysis fits with emerging ML/AI algorithms and the synergies brought by combining the two approaches. We highlight real-world use-cases from across the Pharmaceutical and Life Science sector where SciBite’s text analytics systems have been employed to create next-generation enterprise data infrastructure for many of the world’s leading companies.

Published in: Healthcare
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IC-SDV 2019: Are Ontologies relevant in a Machine Learning World? - Lee Harland (CSO and Founder SciBite, UK)

  1. 1. Are Ontologies Relevant In A Machine Learning World? Dr. Lee Harland Founder & Chief Scientific Officer, SciBite Limited April 2019
  2. 2. The good news is I have discovered inefficiencies… …The bad news is that you are one of them. https://timoelliott.com/blog/cartoons/artificial-intelligence-cartoons
  3. 3. Ontologies Enable Us To Communicate Knowledge Graph Ontology Taxonomy Categorisation Thesaurus Controlled Vocabulary List Human Validated (Consensus/Authority) Machine Understandable Vital to ensure we’re all talking about the same thing! NCBI:txid9365
  4. 4. What Do We Know About “Viagra”? https://bioportal.bioontology.org https://www.ebi.ac.uk/ols
  5. 5. Ontologies Enable Machine Learning/A.I
  6. 6. Ontologies To Interpret ML Output Well Established +100s more use ontologies to interpret output from ML genomics analysis
  7. 7. Ontologies Enhancing Training & Execution … Clinically-driven taxonomy of disease… useful in generating training classes that are both well-suited for machine learning classifiers and medically relevant. …. Taxonomy provides a 2-level validation strategy…. https://www.nature.com/articles/nature21056 doi:10.1038/nature21056
  8. 8. Enhancing Training & Execution (2) doi:10.1038/s41591-018-0335-9 https://www.nature.com/articles/s41591-018-0335-9 https://www.newscientist.com/article/2193361-ai-can-diagnose- childhood-illnesses-better-than-some-doctors/
  9. 9. What’s Good For One…. …The overarching principles in DeepQA are massive parallelism, many experts, pervasive confidence estimation, and integration of shallow and deep knowledge… …In addition to the content for the answer and evidence sources, DeepQA leverages other kinds of semistructured and structured content. Another step in the content-acquisition process is to identify and collect these resources, which include databases, taxonomies, and ontologies, such as dbPedia WordNet, and the Yago ontology… https://www.aaai.org/Magazine/Watson/watson.php
  10. 10. Validation *Ahem* burnsburns burns https://www.scibite.co m/news/of-burns-and- bums-machine- learning-surprises/
  11. 11. © 2018 SciBite Limited Building Ontologies With Machine Learning
  12. 12. Virtuous Circle Of Machine & Human Learning Training Execution Interpretation Enrichment Validation
  13. 13. • Large numbers of disorganised documents (i.e. CRO documents) • Need to align these to internal taxonomy of categories (e.g. M4 hierarchy from FDA) • Also need to identify key pieces of metadata (e.g. what is the study compound? Title? Assay… etc ) • Manual process, incredibly time consuming Pfizer Acquisition Challenge CREDIT: Pfizer Computational Sciences http://www.bio- itworld.com/2018/08/08/a- new-machine-learning- approach-to-document- classification-a-pfizer/scibite- collaboration.aspx
  14. 14. LifeArc Horizon Scanning
  15. 15. A Broader Viewpoint
  16. 16. HELP!
  17. 17. Ontologies Are The Key To Unlocking FAIR ….Therefore, we are confident that the true cost of not having FAIR research data is much higher than the estimated €10.2bn per year... https://publications.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1 https://www.go-fair.org
  18. 18. SciBite Kusp – Making #cleandata easy
  19. 19. F.A.I.R @ AstraZeneca https://www.slideshare.net/n1ck_brown/search-at-astrazeneca-an-agile-appstore-searchbased-apps-created-on-a-rich-search-index https://www.slideshare.net/tplasterer/dataset-catalogs-as-a-foundation-for-fair-data CREDIT: Integrative Informatics CREDIT: R&D Search Team
  20. 20. F.A.I.R @ BMS • Public Bioassay Ontology • Augmented with BMS-specific terms • Users can suggest new assays etc. • Reactive, semantic form fields CREDIT: BMS AIMS Team
  21. 21. SciBite Semantics Empower Scientific Infrastructure F.A.I.R Data Catalogues Knowledge Graphs Analytics & ML/A.I Semantic Q&A Improve Integrity Smart Data Entry Semantic Search Electronic Lab Notebooks L.I.M.S. Assay Registration Asset Management Semantic MDM Ontology Management Departmental Search Enterprise Search Pharmacovigilance Drug Repurposing Horizon Scanning Phenotype Triangulation Outcomes Prediction Portfolio Analytics Clinical Data Search Genomic Data Processing F.A.I.R & Search Data Stewardship Data Mining & AI
  22. 22. Data’s Dynamic Duo Acknowledgements BMS: AIMS Team AZ: R&D Search Team, Integrative Informatics Team, Sinequa Pfizer: Computational Sciences CoE LifeArc All my colleagues at SciBite ML
  23. 23. Thanks! Slides @ https://www.slideshare.net/scibitely Visit Us At http://scibite.com

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