The document summarizes challenges in organizing biological knowledge and progress made through collaborative ontology development. It discusses how early efforts focused on individual ontologies but challenges emerged in maintenance and linking data. New approaches focus on shared principles, standardized mappings between ontologies, and modeling knowledge as graphs. Tools like Boomer and LinkML help reconcile mappings and model data, while community efforts like OBO Foundry and Biolink Model advance integration through open collaboration. Overall progress has been made but more work is needed to operationalize ontologies and build interconnected knowledge graphs.

1. All Together Now: Piecing together the
Knowledge Graph of Life
Chris Mungall
cjmungall@lbl.gov
@chrismungall
ICBO September 2021

2. Outline
● The grand knowledge challenge
○ How can we organize it all?
○ How can we make it accessible?
● How it started:
○ The Open Bio Ontologies Dream
● How it’s going:
○ This is harder than we thought
○ Solving challenges together

3. The grand challenge: making knowledge
computable

4. Data/knowledge is fragmented and inaccessible

5. Biology is hard.
We have many identifiable things and categories.
Drugs 10k
Chemicals 4tn?
Species
~9 million
Diseases and
Phenotypes
10-50k/species
Cells
10,000s+
types
per species)
Experiments
Raw data
?? exabytes
Genes 20k per species
Metagenome dbs: >65bn genes
Genetic
variants
3m in human
alone

6. The things are interconnected across scales

7. Ontologies to the rescue!
I can organize it all
for you!
genes diseases cell types

8. Original problem: too many, all disconnected
Uh oh….
phenotype
anatomy
assays..

9. Open Biological Ontologies (OBO)
http://obofoundry.org
1. Well-integrated
Modular ontologies
2. Provide technical and
sociotechnological
framework for
cooperation
4. Allow us to categorize
and organize all of the
things
3. Tools, best
practices and
infrastructure for
forging new
ontologies
@obofoundry

10. Early days of bio ontologies

11. Standard formats! Hurray!
W3C Semantic Web
Standards
Web Ontology
Language (OWL)

12. Standard Formal Ontologies!
I approve this
ontology
occupies

13. Orthogonal Ontologies! Magnificent!

14. Reviews and Shared Principles!

15. Interoperable Data FTW!
Cool, let’s
interoperate!
Hey, we speak
the same
language!

16. We’re done here, right?

17. Much has indeed been accomplished
15k citations/year
1 billion annotations
10-1000 billion
annotations
Millions of environmental
samples annotated
Diagnosing rare
disease patients
uses
Analyzing
single-cell
seq
You’re
welcome!

18. But there is a lot of heavy lifting
Maintaining ontologies
is hard
I spend most of my
time cleaning and
linking data
Turns out ontologies
aren’t the right tool for
everything
The stuff I need is in
an external database
and not in an ontology
These two ontologies
are incompatible
Which ontology do I
use? How do I extract
what I need?

19. Challenge: Operationalizing OBO
Principles
This ontology is
awesome
This ontology sucks
Half the terms are
missing definitions

20. Approach: The Dashboard and ODK
https://doi.org/10.1101/2021.06.01.446587

21. Challenge: coordinating outside OBO
* Many ontologies have
overlapping content with
other resources.
* How do we reconcile?
CHO: The OBO
cheese
ontology
CheeseTax
CheeseVoc
CheeseBase
CheeseDB

22. GO and reaction/pathway KBs

23. Approach: Technical and
collaborative
Standards:
● SSSOM
○ Simple
○ Standard for
○ Sharing
○ Ontology
○ Mappings
Tools:
● Boomer
○ Bayesian
○ OWL
○ Ontology
○ Merging
● Understand
complementary
curation needs
and scope
● Joint
reconciliation of
mappings
● Targeted
curation

24. Boomer reconciles mapping muddles
⊏ ⊐ ≡ ⋣⋢ ⊏ ⊐ ≡ ⋣⋢ ⊏ ⊐ ≡ ⋣⋢
GO:0000009 EC:2.4.1.232 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04
GO:0000010 RHEA:20836 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04
GO:0000016 EC:3.2.1.108 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04
GO:0000016 METACYC:LACTASE-RXN 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04 0.08 0.08 0.80 0.04
RHEA:66549 REACTOME:R-HSA-176646.3 0.10 0.70 0.15 0.05 0.10 0.70 0.15 0.05 0.10 0.70 0.15 0.05
0.0002048 0.0002048 0.0028672 ✅
Search 1 Search 2 Search 3
Joint probability:
https://github.com/INCATools/boomer
Boomer
○ Bayesian
○ OWL
○ Ontology
○ Merging
Powered by Whelk: scalable Scala OWL reasoner using immutable data structures
Probabilistic extension: searches ‘possible OWL worlds’ for more likely coherent
explanation
Use in reconciliation: finds most likely OWL interpretation of mappings
Works in harmony with ontology developers: pinpoints hotspots of concern
https://github.com/balhoff/whelk

25. Mapping Reactome-> GO CAM
http://geneontology.org/go-cam website
http://rdf.geneontology.org SPARQL
● Algorithm for mapping
BioPAX OWL to GO-
CAM OWL
● Creation of REACTO
ontology
● Iterative process
○ GO curators
○ Reactome
curators

26. Result: Rhea, GO, and Reactome
● Rhea used to create OWL
CHEBI definitions for GO
catalytic activity hierarchy
● Boomer allows for global
reconciliation of mappings
● Mutual improvement in both
resources
● Large increase in annotations
from Rhea->GO mappings
● Rhea used to create OWL
CHEBI definitions for GO MF
terms
● Boomer allows for global
reconciliation of mappings
● Mutual improvement in both
resources
● Large increase in annotations
from Rhea->GO mappings

27. Challenge: Our abstractions are hard
Upper ontologies contain unfamiliar
terms and requires care when
applying not to fall into traps
OWL is essential for automating
construction tasks but mistakes when
authoring OWL axioms are very
common

28. Solution: Buffer from abstractions
Buffering upper
ontology:
● COB: Core
Ontology for
Biology and
Biomedicine
OWL Buffering:
● Templated OWL
Design Patterns
● Training
○ E.g ICBO
● Open
discussions
● Communication
(slack)

29. An upper ontology buffer
subatomic particle electron (CHEBI)
atom or ion
molecular entity
macromolecular
entity
cellular component
cell
gross anatomical
structure
organism
population
nickel (CHEBI)
aspirin (CHEBI)
actin (PR)
cell nucleus (GO)
macrophage (CL)
lung (UBERON)
Human (NCBITaxon)
Cohort (PCO)
Scale
Domain
ontology term
Get Involved!
https://github.com/OBOFoundry/COB
COB term
● Common Ontology for
Biology/Biomedicine
(COB)
● A shared layer of terms
beneath BFO
○ (Still compatible!)
● COB Workshop at ICBO
○ Ready for early
adopters!

30. Templated OWL Design Patterns
https://robot.obolibrary.org/template
https://github.com/INCATools/dead_simple_owl_design_patterns/
‘Transmembrane transport’
and has-start-location some
___cellular component and has-end-
location some ___cellular
component and transports some
__molecular entity
Acts as an OWL buffer
dct:creator
dct:contributor
OWL
expert
Ontology developer
Domain expert
dct:contributor
Ontology
developer
Biocurator
ROBOT +
Elk

31. Ontology Users
Ontology
Developer
s
OWL
experts
● Author OWL templates
● Create Design Patterns
● Implement OWL templates
● Test against Design Patterns
● Consume pre-
reasoned hierarchies
Leverage the Expertise Pyramid
Learning

32. pattern-
workshop
Phenotype OWL
pattern reconciliation
We need a pattern for
an abnormal biological
process in a location.
upheno-
dev
patterns
Judging from the comments
the pattern is usable now,
moving to development stage.
More than 5 community
members have rated this
pattern as finished.
1. Pattern proposal 2. Pattern discussion 3. Pattern review

33. ● Exomiser is a
variant
prioritization tool
● Infers causative
gene in an
inherited disorder
● Uses genome and
phenotypic
(ontology)
information from
different species
● Deployed in
clinical setting
and applied on
multiple real-
world cases (e.g.
Genomics
England)

34. Challenge: Terms are not enough
Incompatible
Schemas !

35. Linked Data Modeling Language
JSON-Schema
ShEx
JSON-LD
Contexts
Python
Dataclasses
OWL
https://linkml.io
https://github.com/linkml/linkml
Semantic Web
Applications
And
Infrastructure
“Traditional”
Applications and
Infrastructure
SQL DDL
Create datamodels in simple YAML files,
optionally annotated using ontologies
Compile to other
frameworks
Choose the right tools
for the job, no lock in
Biocurator
Data
Scientist
dct:creator

36. Use in cancer data harmonization
Clinical
Terminologies
OBO Ontologies
https://cancerdhc.github.io/ccdhmodel
Cancer Research Data
Commons (CRDC)
Harmonized Data Model
● Modeling team
● Terminology team
● Unified framework
SSSOM

37. Use for microbiome data
https://microbiomedata.github.io/nmdc-schema/
Metadata standards to enable
microbiome analysis
● Environmental data
● Omics data
● Community development model

38. Challenge: Knowledgebases in OWL
???
Which OWL constructs
are appropriate?
How to model?
Knowledgebases (KBs): curated or computed
statements relating biological entities (e.g.
MODs, DrugBank, ENSEMBL, ...)

39. Challenge: Knowledgebases in OWL
https://doi.org/10.1016/j.yjbinx.2019.100002

40. Challenge: Knowledgebases in OWL

41. Challenge: Knowledgebases in OWL
http://ceur-ws.org/Vol-222/krmed2006-p05.pdf

42. Approach: Knowledge Graphs

43. KGs: Same but different
https://douroucouli.wordpress.com/2019/03/14/biological-knowledge-graph-modeling-design-patterns
Edges >> Axioms Embedding >> Reasoning
TSV >>
RDFStar >>
RDF/OWL
● Like ontologies, KGs organize knowledge
● Difference in emphasis
○ Things not categories
○ Edges not hierarchies or axioms
○ Machine learning and graph
algorithms
● Toolchains reflect these differences
● Better for modeling ‘omics entities’?

44. Owlstar: Buffered semantics in KGs
OWL
expert
Data
Scientist
:Finger
subClassOf
:partOf some
:Hand
[ a :lmo-2 ]
interactsWith
[ a :elf-2 ]
Existential restriction
pattern
Blank node pattern
KG World: Simple Edges
https://github.com/cmungall/owlstar

45. Knowledge Graph Hub
eco-KG
KG-OBO
KG-COVID-19
KG-IDG
KG-Envpolyreg
https://knowledge-graph-hub.github.io
KG-Microbe
https://kgx.readthedocs.io
○ KGHub: Registry of KGs and
lightweight integration
○ KGX: Graph building and
manipulation toolkit
○ Ensmallen: graph library - SOTA
loading/manipulating graphs -
minimal time and memory
footprint
○ Embiggen: graph machine
learning based on Ensmallen,
tensorflow
○ Algorithms: node2vec, TransE
and friends, GCN/GNN, some
novel algorithms

46. Biolink-Model: A schema for biological KGs
● Expressed in LinkML
● “Ontology-like”
● OBO classes are instances
● Edges are first-class
Working together: Biolink Model
https://biolink.github.io/biolink-model
Developed as part of NCATS Translator project
● Weekly Data Modeling calls (20-40 people)
● Working groups for different areas (e.g.
chemicals)
● Technically diverse group (domain
scientists, bioinformaticians, ontologists)
● Use of GitHub (PRs, votes)

47. KGs for drug repurposing
SARS-CoV-2
drugs
A. Node2Vec -
Embed in low-
dimensional
space
C. Train neural
network for
link prediction
B. Rank drugs by
cosine similarity
to SARS-CoV-2
Ranked list of repurposing
candidates
Classes of drugs in the top 100 of
ChEMBL antivirals:
● Steroids - dexamethasone,
prednisone
● Antivirals - lopinavir,
remdesivir
● Antibiotics (?) -
benzylpenicillin, ciprofloxacin
● COX inhibitors - aspirin,
ibuprofen, acetaminophen

48. We have a long way to go...

49. We can do it together!

50. Acknowledgments
OBO Operations
● Mathias Brochhausen
● Pier Luigi Buttigieg
● Melanie Courtot
● Alexander Diehl
● Melissa Haendel
● Simon Jupp
● Nomi Harris
● James Malone
● Darren Natale
● Jim Balhoff
● David Osumi-Sutherland
● Philippe Rocca-Serra
● Asiyah Lin
● Damion Dooley
● Alan Ruttenberg
● Richard Scheuermann
● Lynn Schriml
● Barry Smith
● Chris Stoeckert
● Nicole Vasilevsky
● Ramona Walls
● Xiaolin Yang
● Jie Zheng
OBO Services Team
● James Overon
● Becky Jackson
● Nico Matentzoglu
● Seth Carbon
● Mark Miller
● Deepak Unni
● Nomi Harris
● Bill Duncan
● Randi Vita
● Bjoern Peters
We’re hiring!!!
Knowledge Graphs
● Justin Reese
● Marcin Joachimiak
● Bill Duncan
● Seth Carbon
● Harshad Hegde
● Harry Caufield
● Sierra Moxon
● Elena Casiraghi
● Luca Cappelletti
● Giorgio Valentini
● Tommaso Fontana
● Tiffany Callahan
● Kent Shefchek
● Kevin Schafer
● Nomi Harris
● Moni Muñoz-Torres
● Peter Robinson
GO/Reactome/Rhea
● Peter d’Eustachio
● Harold Drabkin
● Jim Balhoff
● Ben Good
● Huaiyu Mi
● David Hill
● Kimberly van Auken
● Pascale Gaudet
● Laurent-Philippe Albou
● Anne Morgat
● Alan Bridge
● Paul Thomas
LinkML
● Harold Solbrig
● Dazhi Zhao
● Joe Flack
● Gaurav Vaidya
● Tim Putman
● Donny Winston
● Bill Duncan
● Mark Miller
● Sujay Patil
● Shahim Essaid
● Matt Brush
● Brian Furner
● Sierra Moxon Patterns
● Sue Bello
● Nicole Vasilevsky
● All the MOD + HPO curators
● Nico Matentzoglu
BioLink
● Sierra Moxon
● Mike Bada
● Deepak Unni
● Michel Dumontier
● Vlado Dancik
● Matt Brush
● NCATS Translator DM
team