Slides i presented at the pistoia alliance webinar on ontologies http://www.pistoiaalliance.org/pistoia-alliance-debates-webinar-series/ (Feb 2017)

1. ©PistoiaAlliance
Ontologies In The SciBite Platform
123 February, 2017
Lee Harland | @SciBitely | www.scibite.com

2. ©PistoiaAlliance
223 February, 2017
80-90% of all potentially usable
business information may
originate in unstructured form
https://en.wikipedia.org/wiki/Unstructured_data

3. ©PistoiaAlliance
‘Semantics-as-a-Service’
323 February, 2017
Text Content
Documents & Databases
Ontologies:
Gene/Disease/Drug; Molecular;
Chemical; Clinical; Adverse Event;
PharmSci & Manufacturing; Business
& Commercial; Regulatory; Geo-
location; University/Company
Structured Data
+
SciBite
API

4. ©PistoiaAlliance
Public Ontologies Are Vital
423 February, 2017
What They Are Great For
• Providing a open, consistent, stable
identifier for a given “thing”
• Developing community consensus as to
what that ”thing” is
• Developing community consensus on what
all the things are
• Powering Data Integration
• Powering Scientific Analytics
Not Designed For Text Analytics/Mining

5. ©PistoiaAlliance
3 Key Issues
523 February, 2017
e.g. Human Phenotype Ontology
(HPO) is a gold reference standard for
phenotypes and many use cases start
with “find all the phenotypes….”
But 6997 synonyms in current HPO
over 11375 entities. Similar for many
others as not their raison d'etre
1. Synonym Coverage
2. Coding Style 3. Ambiguity

6. ©PistoiaAlliance
Ontology Engineering
623 February, 2017
Raw
Ontologies
Public SciBite
Customer
Expert
Curation
Training, Testing,
& Validation
TERMite
Feedback To
Producers
Automated Learning,
Enrichment & Curation

7. ©PistoiaAlliance
SciBite Ontology Enrichment*
SciBite
HPO
MeSH
HGNC
Original
* Actual search space many fold larger due to adaptive matching

8. ©PistoiaAlliance
Ontology-Driven Search
823 February, 2017

9. ©PistoiaAlliance
Ontology-Driven Search
Everywhere!
923 February, 2017
SciBite
Researchers
(End User)
Information
Management
(Content Awareness)
Informatics
(Text Mining)
System Developers
(3rd Party Integration)
Pharma’s Internal
Apps
Commercial
Content/Software
Providers
Competitor Intelligence
Literature Awareness,
Trends & Alerting
Align 3rd Party Content
Disease & Phenotype Networks,
PPIs, Pharmacovigilance,
Drug-Target/Biomarker Mining
Patent Mining
Machine Learning/AI
Document Management,
Enterprise & Local Search,
Ontology-XREF
Semantic Auto-complete
RDF & Integration
Customer-Provider Integration
Ontology-driven applications
Smarter Apps!

10. ©PistoiaAlliance
Summary
1023 February, 2017
• Text (Databases & Documents) accounts for large
amount of corporate “knowledge”
• Public & Internal Ontologies have great potential in
structuring this text into minable data
• But these ontologies require significant processing, both
human and automated in order to make them “fit for
purpose”
• Combine this with a fast, flexible, simple API and you
can address a vast array of different use cases in
– Software Vendors & Systems Developers
– Content Providers
– Data Scientists & Text Miners