20141112 courtot big_datasemwebontologies

Big
data,
Seman-c
Web
and
Ontologies
Mélanie
Courtot,
PhD
Nov
12th
2014
mcourtot@sfu.ca
1

Overview
3
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Pharmacovigilance
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

Overview
4
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

Big
data
Big
data
is
data
that
is
too
large
and
complex
to
process
for
any
convenHonal
data
tools.
6

What
is
a
Ze^abyte?
1,000,000,000,000
gigabytes
1,000,000,000,000
terabytes
1,000,000,000,000
petabytes
1,000,000,000,000
exabytes
1,000,000,000,000
zeAabyte
9

How
big
is
big?
• Facebook:
25
Terabytes
of
logged
data
per
day,
Google
(2008):
20
Petabytes
per
day
• Over
90%
of
all
the
data
in
the
world
was
created
in
the
past
2
years
[1]
• Today
3.2
ze^abytes.
2020:
40
zeAabytes.[2]
• Good
news:
jobs!
[3]
1. http://www-01.ibm.com/software/data/bigdata/
2. http://barnraisersllc.com/2012/12/38-big-facts-big-data-companies/
10
3. http://www.webopedia.com/quick_ref/important-big-data-facts-for-it-professionals.html

11
h^ps://hbr.org/2012/10/data-‐scienHst-‐the-‐sexiest-‐job-‐of-‐the-‐21st-‐century

12
Issues
with
research
data
(1):
data
availability
h^p://www.nature.com/news/scienHsts-‐losing-‐data-‐at-‐a-‐rapid-‐rate-‐1.14416

Issues
with
research
data
(2):
data
reproducibility
h^p://www.firstwordpharma.com/node/931605#axzz3IalL2lzU
13

Overview
14
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• Seman-c
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

A
soluHon:
the
SemanHc
Web
"The
Seman*c
Web
is
an
...
extension
of
the
current
web
in
which
...
informa*on
is
given
well-‐defined
meaning,
...
be?er
enabling
computers
and
people
to
work
in
coopera*on.”
The
Seman)c
Web
Tim
Berners-‐Lee,
James
Hendler
and
Ora
Lassila
ScienHfic
American,
May
2001
http://www.scientificamerican.com/article/the-semantic-web/15

The
SemanHc
Web
in
a
nutshell
Adds
to
Web
standards
and
prac*ces
(currently
only
for
documents
and
services)
encouraging
• Unambiguous
names
for
things,
classes,
and
relaHonships
• Well
organized
and
documented
in
ontologies
• With
data
expressed
using
uniform
knowledge
representaHon
languages
(e.g.
OWL)
• To
enable
computaHonally
assisted
exploitaHon
of
informaHon
• That
can
be
easily
integrated
from
different
sources
16

Some
SemanHc
Web
successes
• In
February
2011,
the
Watson
system
by
IBM
made
internaHonal
headlines
for
beaHng
the
best
humans
in
the
quiz
show
Jeopardy!
• A
significant
number
of
very
prominent
websites
are
powered
by
Seman-c
Web
technologies,
including
the
New
York
Times,
Thomson
Reuters,
BBC,
and
Google's
Freebase.
• The
Speech
Interpreta-on
and
Recogni-on
Interface
Siri
launched
by
Apple
in
2011
as
an
intelligent
personal
assistant
for
the
new
generaHon
of
IPhone
smartphones
heavily
draws
from
work
on
ontologies,
knowledge
representaHon,
and
reasoning.
17
h^p://130.108.5.60/faculty/pascal/pub/crc-‐handbook-‐13.pdf

Uniform
Resource
IdenHfiers
(URIs)
• Two
different
uses:
– Unambiguous
name
for
something
– LocaHon
of
a
document
• Examples:
– h^p://example.org/wiki/Main_Page
– sp://example.org/resource.txt
– mailto:someone@example.com
19

Resource
DescripHon
Framework
(RDF)
• Resources (= nodes)
• Identified by Unique Resource Identifier (URI)
• Properties (= edges)
• Identified by Unique Resource Identifier (URI)
• Binary relations between 2 resources
20
h^p://elmonline.ca/sw/sparql/social.^l

<h^p://www.linkedin.com/in/mcourtot>
a
foaf:Person
;
foaf:name
"Melanie
Courtot"
;
foaf:knows
<h^p://elmonline.ca/luke>
;
foaf:knows
<h^p://www.linkedin.com/pub/mark-‐wilkinson/1/674/665>
.
21

SPARQL
SELECT
?person
WHERE
{
<h^p://www.linkedin.com/in/mcourtot>
<h^p://xmlns.com/foaf/0.1/knows>
?person
.
}
-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐
|
person
|
==========================================================
|
h^p://www.linkedin.com/pub/mark-‐wilkinson/1/674/665
|
|
<h^p://elmonline.ca/luke>
|
-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐
• An
excellent
tutorial
by
Luke
McCarthy:
h^p://elmonline.ca/sw/sparql/
22
A
query
language
for
RDF

The
Web
Ontology
Language
(OWL)
• Knowledge
representaHon
language
• Based
on
DescripHon
Logics:
fragments
of
First-‐Order
logics
with
decidable
and
defined
computaHonal
properHes
• Sound,
complete,
terminaHng
reasoners
available
23

Overview
24
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• Seman-c
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

Biological
resources
in
LOD
26

Examples
of
issues
in
linking
data
incorrectly
• h^p://dbpedia.org/resource/Welsh
OWL:sameAs
<h^p://sw.cyc.com/2006/07/27/cyc/EthnicGroupOfWelsh>
<h^p://sw.cyc.com/2006/07/27/cyc/Welsh-‐TheWord>
<h^p://sw.cyc.com/2006/07/27/cyc/WelshLanguage>
<h^p://sw.cyc.com/2006/07/27/cyc/Welshing-‐Chea-ng>
27

Overview
28
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– Defini-on
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

Ontologies
• RepresentaHon
of
important
things
in
a
specific
domain
– Describes
types
of
enHHes
(e.g.
cells)
and
relaHons
between
them
(e.g.
prokaryoHc
cells
and
eukaryoHc
cells
are
cells)
and
their
instances
(e.g.
the
specific
cells
in
my
sample)
• An
acHve
computaHonal
arHfact
– A
mathemaHcal
model
based
on
a
subset
of
first
order
logic
– Tools
can
automaHcally
process
ontologies
• A
communicaHon
tool
– Provides
a
dicHonary
for
collaborators,
a
shared
understanding
– Allows
data
sharing
29

Reasoning
is
criHcal
• ProkaryoHc
and
EukaryoHc
cell
are
declared
disjoints
• Fungal
cell
is
a
EukaryoHc
cell
• Spore
is
a
Fungal
cell
and
a
ProkaryoHc
cell
⇒ InsaHsfiability
⇒ SoluHon:
clarify
spore
(sensu
Mycetozoa)
AND
acHnomycete-‐type
spore
h^p://www.plosone.org/arHcle/info:doi/10.1371/journal.pone.0022006
30

Logics
• Simple
example
based
on
h^p://arxiv.org/pdf/1201.4089v1.pdf
• Ontology
file
available
from
h^p://www.sfu.ca/~mcourtot/course/
20141112BigDataSemWebOntologies/
ontology.owl
• ManipulaHon
done
using
Protégé:
h^p://protege.stanford.edu
31

Logics
of
a
grandfather
33

Inferred
class
hierarchy
35

Overview
39
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exis-ng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

OBO
Foundry
A
subset
of
biological
and
biomedical
ontologies
whose
developers
have
agreed
in
advance
to
accept
a
common
set
of
principles
reflecHng
best
pracHce
in
ontology
development
designed
to
ensure
• Hght
connecHon
to
the
biomedical
basic
sciences
• CompaHbility
• interoperability,
common
relaHons
• formal
robustness
• support
for
logic-‐based
reasoning
40

RELATION
TO TIME
GRANULARITY
CONTINUANT
OCCURRENT
INDEPENDENT
DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy?)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO)
Phenotypic
Quality
(PaTO)
Organism-‐‑Level
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
Cellular Process
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular
Process
(GO)
42
Slide
credit:
Barry
Smith

Minimum
InformaHon
to
Reuse
an
External
Ontology
Term
• OBO
and
SemaHc
Web
promote
reuse
of
resources
• Biological
resources
(e.g.,
FMA
for
anatomy),
taken
together,
are
too
big
for
current
tool
support.
• MIREOT
used
across
the
OBO
library
– OBI:
400
mireoted
terms
(140
GO,
55
ChEBI,
50
PATO)
– PR
(Protein
Ontology):
23,000
mireoted
terms
• h^p://ontofox.hegroup.org
43

Example
of
OBO
ontologies
• OBI,
Ontology
for
Biomedical
invesHgaHons
• VO,
the
vaccine
ontology
• AERO,
the
Adverse
Event
ReporHng
Ontology

Ontology
for
Biomedical
InvesHgaHons
(OBI)
• OBI
is
a
mulH-‐community
project
driven
by
the
pracHcal
needs
of
its
members
with
the
goal
to
build
a
high
quality,
interoperable
reference
ontology
• OBI
high
level
classes
are
in
place
-‐
solidified
over
several
years
-‐
that
cover
all
aspects
of
biomedical
invesHgaHons
• OBI
is
expanded
to
enable
member
applicaHons
and
based
on
term
requests
45

46
High
level
class
hierarchy
(parHal)
Slide
credit:
OBI
Consor)um

47
Slide
credit:
Alan
Ru=enberg

48
Slide
credit:
OBI
Consor)um

49
RepresenHng
vaccine
data
–
the
Vaccine
Ontology
(VO)
Picture
credit:
Yongqun
He

Overview
50
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

RepresenHng
pharmacovigilance
data
• The
Adverse
Event
ReporHng
Ontology
(AERO)
• Encodes
exisHng
clinical
guidelines
(Brighton
CollaboraHon)
Patient examination
Anatomical system
dermatological
system
Clinical Finding
about mre
involves
Patient of rash
Clinical Report
has specified output
has participant
finding
exam report of
June 7
rash
Medically
relevant entity
has specified input
'found to exhibit' some 'generalized urticaria or generalized erythema finding'
'found to exhibit' some 'measured hypotension finding'
inferred to be of type
inferred to be of type
major dermatological criterion
for anaphylaxis according to Brighton
major cardiovascular criterion
for anaphylaxis according to Brighton
has component has component
Level 1 of certainty of anaphylaxis according to Brighton
part of
located in
Clinician
has participant
is about
found to exhibit
51

Background
and
problem
statement
• Surveillance
of
Adverse
Events
Following
Immuniza-on
is
important
– DetecHon
of
issues
with
vaccine
– Importance
of
vaccine-‐risk
communicaHon
• Analysis
of
AE
reports
is
a
subjec-ve,
-me-‐
and
money
costly
process
– Manual
review
of
the
textual
reports
52

Workflow
• Hypothesis:
Use
the
AERO
I
developed
to
annotate
and
classify
a
dataset
• VAERS
dataset
– Vaccine
Adverse
Event
ReporHng
System
– 6032
reports:
~5800
negaHve,
~230
posiHve
– Post
H1N1
immunizaHon
2009/2010
– Manually
classified
for
anaphylaxis
• MedDRA
(Medical
DicHonary
of
Regulatory
AcHviHes)
is
used
to
represent
clinical
findings
53

54
Automated
Diagnosis
workflow
MANUALLY
CURATED
DATASET A
ADVERSE EVENT
REPORTING ONTOLOGY
(AERO)
OWL/RDF
EXPORT
VAERS DATASET
MySQL
BRIGHTON
ANNOTATIONS
ASCII files MySQL
REASONER
~800 MedDRA terms mapped to 32 Brighton terms
?
B
C
D

55
Results
MANUALLY
CURATED
DATASET A
ADVERSE EVENT
REPORTING ONTOLOGY
(AERO)
OWL/RDF
EXPORT
VAERS DATASET
MySQL
BRIGHTON
ANNOTATIONS
ASCII files MySQL
REASONER
~800 MedDRA terms mapped to 32 Brighton terms
?
B
C
D
At
best
cut-‐off
point:
Sensi-vity
57%
Specificity
97%

3
months
manual
56
AE
classificaHon
can
be
improved
through
the
use
of
ontologies
Time gain
Legend
Manual analysis
Ontology-based
analysis
November 2009 December 2009 January 2010
Ability to
detect signal
6000
reports
• Manual
Time
2h
automated
analysis:
3
months
for
12
medical
officers
• Ontology-‐based
vs.
analysis:
once
data
collected
(2
months),
almost
instantaneous
(2h
on
laptop)
=>
Could
allow
for
earlier
detecHon
of
safety
issues
and
be^er
understanding
of
adverse
events
h^p://dx.doi.org/10.1371/journal.pone.0092632

Overview
57
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
Seman-c
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

59
Ontobee:
publishing
biomedical
resources
on
the
SemanHc
Web
HTML
for
humans
…

Ontobee:
publishing
biomedical
resources
on
the
SemanHc
Web
…
RDF
for
machines

Overview
61
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaborm
– Adding
standards
to
IRIDA
• Take
home
message

The
Integrated
Rapid
InfecHous
Disease
Analysis
(IRIDA)
project
• Goal:
automate
infecHous
disease
outbreak
detecHon
and
invesHgaHon
• Issues:
– Integrate
WGS,
clinical
and
lab
info
– Provide
relevant
tools
and
validate
pipeline
• Methods:
– Data
standards
for
informaHon
exchange
– Analysis
pipeline
(Galaxy
based)
– User
interface
– AddiHonal
tools:
• IslandViewer
• GenGIS
62

Building
the
IRIDA
data
standards
• Interview
with
key
personnel
at
BCCDC
• Review
of
exisHng
resources
• IdenHfy
“holes”,
i.e.,
missing
bits
• Collect
exisHng
data
• Liaise
with
implementaHon
team
• Generate
cohesive
resource
• Validate
64

Relevant
data
standards
• TypON,
the
typing
ontology
• OBI,
the
ontology
for
Biomedical
InvesHgaHons
• NGSOnto,
Next
GeneraHon
Sequencing
Ontology
• NIAIS-‐GS-‐BRC
core
metadata
• TRANS,
Pathogen
Transmission
ontology
• ExO,
Exposure
Ontology
• EPO,
Epidemiology
Ontology
• IDO,
InfecHous
Disease
Ontology
• Food:
USDA,
EFSA?
65

Relevant
internaHonal
efforts
• MIxS
standard
• Global
Microbial
IdenHfier
• Global
Alliance
for
Genomics
and
Health
• NCBI
BioSample
• European
NucleoHde
Archive
• …
66

Remaining
challenges
• Trust,
provenance
– Ability
to
track
origin
of
data
to
assess
whether
it
is
trustworthy
• Data
sharing,
reuse,
policy
– Social
and
legal
issues
in
ge…ng
access
to
data
• ConfidenHality
– Privacy
concerns
when
linking
data
67

Overview
68
• Big
Data
– Big
Data
is
BIG
– Issues
in
research
• SemanHc
Web
– Standards:
URIs,
RDF,
SPARQL,
OWL
– Linked
data
• Ontologies
– DefiniHon
and
reasoning
– OBO
Foundry
– Example
of
exisHng
ontologies
– Publishing
ontologies
on
the
SemanHc
Web
• IRIDA
– The
IRIDA
plaXorm
– Adding
standards
to
IRIDA
• Take
home
message

Take
home
message
Big
data
is
a
big
challenge,
but
we
can
deal
with
it
if
done
properly:
that
will
be
your
responsibility
DO
NOT
build
a
black
box
DO
annotate
and
describe
your
data
DO
make
your
data
openly
available
69

Acknowledgements
• Drs.
Fiona
Brinkman,
Will
Hsiao,
Ryan
Brinkman
• The
Brinkman^2
labs
• Alan
Ru^enberg,
Barry
Smith,
Chris
Mungall
&
OBO
• Colleagues
at
Public
Health
Agency
Canada
(Ms
Lafleche,
Dr
Law)
• The
IRIDA
consorHum
and
the
IRIDA
ontology
working
group
(Emma
Griffiths
and
Damion
Dooley)
70

71
Mélanie
Courtot,
PhD
mcourtot@sfu.ca
@mcourtot
h^p://purl.org/net/mcourtot

20141112 courtot big_datasemwebontologies

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