RDF: what and why plus a SPARQL tutorial

RDF what and why
Jerven Bolleman
Developer
Swiss-Prot Group

Introduction
• RDF

• Its
a
technology

• Cost
and
affordability
are
key
concerns

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<++.>+++++[<+++++++++++++>-]<.+++++++++++++.----------.>++++
+++[<---------->-]<++.>++++++++[<++++++++++>-]<.>+++[<----->
-]<.>+++[<++++++>-]<..>+++++++++[<--------->-]<--.>+++++++[<
++++++++++>-]<+++.+++++++++++.>++++++++[<----------->-]<++++
.>+++++[<+++++++++++++>-]<.>+++[<++++++>-]<-.---.++++++.----
---.----------.>++++++++[<----------->-]<+.---.[-]<<<->[-]>[
-]<<[>+>+<<-]>>[<<+>>-]>>>[-]<<<+++++++++<[>>>+<<[>+>[-]<<-]
>[<+>-]>[<<++++++++++>>>+<-]<<-<-]+++++++++>[<->-]>>+>[<[-]<
<+>>>-]>[-]+<<[>+>-<<-]<<<[>>+>+<<<-]>>>[<<<+>>>-]<>>[<+>-]<
<-[>[-]<[-]]>>+<[>[-]<-]<++++++++[<++++++<++++++>>-]>>>[>+>+
<<-]>>[<<+>>-]<[<<<<<.>>>>>-]<<<<<<.>>[-]>[-]++++[<++++++++>
-]<.>++++[<++++++++>-]<++.>+++++[<+++++++++>-]<.><+++++..---
-----.-------.>>[>>+>+<<<-]>>>[<<<+>>>-]<[<<<<++++++++++++++
.>>>>-]<<<<[-]>++++[<++++++++>-]<.>+++++++++[<+++++++++>-]<-
-.---------.>+++++++[<---------->-]<.>++++++[<+++++++++++>-]
<.+++..+++++++++++++.>++++++++[<---------->-]<--.>+++++++++[
<+++++++++>-]<--.-.>++++++++[<---------->-]<++.>++++++++[<++
++++++++>-]<++++.------------.---.>+++++++[<---------->-]<+.
>++++++++[<+++++++++++>-]<-.>++[<----------->-]<.+++++++++++
..>+++++++++[<---------->-]<-----.---.+++.---.[-]<<<]
@

What is RDF?
What?
Why?
SPARQL?
Exam
ples
Exam
ples

RDF: Resource Description Framework
• Resource
– Generalization of “Web resource”
– A thing that can be identified (but not necessarily
retrieved) on the Web
• Description
– A resource is described with statements that
specify the properties and property values of the
resource
• Statement (aka Triple)
– subject: identifies the resource
– predicate: identifies a property of the resource
– object: identifies the value of that property

Everything can be described with (loads
of) triples...
Subject
Property
(resource)
A Triple
Object
(resource
or
literal value)
Subject
(resource)

Related triples form a graph...

An RDF graph can be serialized in several
ways
• RDF/XML: the W3C’s official format
– XML is well established: good for application developers
– very verbose, not very “readable”
– e.g. uniprot.org/uniprot/P00750.rdf
• N-Triple
– good for loading into triple stores
– e.g. uniprot.org/uniprot/P00750.nt
• Turtle ⟵ most examples will use this
– good for reading by humans
– e.g. uniprot.org/uniprot/P00750.ttl
• JSON-LD
– easy for javascript/websites
• ....
• Conversion 100% lossless

A simple example
RDF
What and why
presented by
A Triple
“Jerven Bolleman”
Literal value

RDF identifies resources with URIs
UniProt.rdf
What and why
presented by
A Triple
expasy.org/people/
Jerven_Tjalling
.Bolleman.htm
URI

Multiple URIs may identify the same thing
expasy.org/people/
Jerven_Tjalling
.Bolleman.htm
ch.linkedin.com/
in/jervenbolleman
owl:sameAs
A Triple

The life sciences have an identity
problem...
• www.genenames.org/data/hgnc_data.php?
hgnc_id=9993
– RGS11: regulator of G-protein signaling 11
• http://www.uniprot.org/taxonomy/9993
– European alpine marmot
• ...
Text
Te What is “9993”?

Hello, I a 9993.
I like flower?

The solution are URIs
• In RDF statements:
– subject and predicates must be URIs
– objects may be URIs or literal values 
• Advantages:
– No risk of “name clashes” when integrating data from
different sources
– Different people can make statements about the same
resource: 
Distributed annotation at a global scale!

Example: From tab-delimited to semantic
RDF in Turtle
format
Tab delimited Converted To
An example

Example: From tab-delimited to semantic
A Triple
Q9VGZ4
P25724
Q9V3H7
Q00403
P23312
P31928
Q9NAE1
Q9TYY1
Q10666
Q21921
Interactions.txt
P32234
P32234
P32234
P42643
P42643
P42643
P41932
P41932
P41932
P41932

Example step 1: Use URIs for subjects
and objects
A Triple
Interactions.txt
...prot/Q9VGZ4
...prot/P25724
...prot/Q9V3H7
...prot/Q00403
...prot/P23312
...prot/P31928
...prot/Q9NAE1
...prot/Q9TYY1
...prot/Q10666
...prot/Q21921
purl.uniprot.org/uniprot/P32234
...prot/P42643
...prot/P42643
...prot/P42643
...prot/P41932
...prot/P41932
...prot/P41932
...prot/P41932

Example step 2: Use shorthand syntax
A Triple
Interactions.txt
prot:Q9VGZ4 .
prot:P25724 .
prot:Q9V3H7 .
prot:Q00403 .
prot:P23312 .
prot:P31928 .
prot:Q9NAE1 .
prot:Q9TYY1 .
prot:Q10666 .
prot:Q21921 .
@prefix prot:<purl.uniprot.org/uniprot/>
prot:P32234
prot:P32234
prot:P32234
prot:P42643
prot:P42643
prot:P42643
prot:P41932
prot:P41932
prot:P41932
prot:P41932

Example step 3: Make statements
A Triple
Interactions.txt
prot:P32234
prot:P32234
prot:P32234
prot:P42643
prot:P42643
prot:P42643
prot:P41932
prot:P41932
prot:P41932
prot:P41932
interacts_with
interacts_with
interacts_with
interacts_with
interacts_with
interacts_with
interacts_with
interacts_with
interacts_with
interacts_with
prot:Q9VGZ4 .
prot:P25724 .
prot:Q9V3H7 .
prot:Q00403 .
prot:P23312 .
prot:P31928 .
prot:Q9NAE1 .
prot:Q9TYY1 .
prot:Q10666 .
prot:Q21921 .

Example step 4: Use URIs for properties
@prefix core:<purl.uniprot.org/core/>
prot:P32234
prot:P32234
prot:P32234
prot:P42643
prot:P42643
prot:P42643
prot:P41932
prot:P41932
prot:P41932
core:interacts_with
core:interacts_with
core:interacts_with
core:interacts_with
core:interacts_with
core:interacts_with
core:interacts_with
core:interacts_with
core:interacts_with
Interactions.ttl
prot:Q9VGZ4 .
prot:P25724 .
prot:Q9V3H7 .
prot:Q00403 .
prot:P23312 .
prot:P31928 .
prot:Q9NAE1 .
prot:Q9TYY1 .
prot:Q10666 .

RDF What? Quick recap
• RDF describes data with statements (aka triples)
– statement = subject + predicate + object
– related statements form a directed graph
• RDF uses URIs to identify things:
– subject and predicates must be URIs
– objects may be URIs or literal values
• Multiple serialisation formats that are 99.999999%
automatically convertible

Why RDF? Isn’t there a simpler solution?
What?
Why?
SPARQL?
Exam
ples
Exam
ples

A very simple example: FASTA
• Why does everyone in the sequence world use
FASTA?

A very simple example: FASTA
• Why does everyone in the sequence world use
FASTA?
– The smallest common denominator
– You can put in the header what you like and I can
choose to ignore it
• BUT: You only get a sequence...
>Who|cares_about:this?
THISISWHATWEWANT

A simple example: GFF
• Some people want to exchange more than
sequences, and invented GFF:
• BUT: ...
SEQ1 EMBL atg 103 105 . + 0
SEQ1 EMBL exon 103 172 . + 0

A simple example: GFF
• Some people want to exchange more than
sequences, and invented GFF:
• BUT: What do the columns mean?
– Originally, an exchange format for sequence
feature descriptions, later also used for other
annotations
– 3 versions known (to me ;)
– Not extendable without prior agreement of all
users
SEQ1 EMBL atg 103 105 . + 0
SEQ1 EMBL exon 103 172 . + 0

A proper solution: XML
• There is a world beyond sequences and
bioinformatics! 
• XML is an IT-industry standard
– Datatypes
– Multi namespaces
– Schemas 
• BUT:
– Hierarchical data model
– Schemas close extension

XML represents data as a tree
• XML datatypes
– Multi namespace
– XML Schema closes extensions
• Tree format
entry
Proton
acceptor 196
activ
e
2.7.11.
-
EC

No XML standard for other relationships
prizes:a case study
• XML datatypes
– Multi namespace
– XML Schema closes extensions
• Tree format
entry
Proton
acceptor 196
activ
e
2.7.11.
-
EC

Our data is a graph!
entry
Proton
acceptor
196activ
e
2.7.11.
-
EC

RDF advantages
• W3C standard
• Can be serialized as XML or JSON
• i.e. most benefits of XML or JSON
• Generic graph structure
• URIs as a standard way to identify resources and
their properties
– data integration without name clashes
– distributed annotation
– normalization
• Extensible!

RDF is extensible
• Anyone can say Anything about Anything
– You can say something about my data
• RDF extensions remain compatible
• RDF encourages data and schema reuse
@prefix intact:<fake.ebi.ac.uk/intact/example>
prot:P32234
prot:P32234
intact:interacts_with
intact:interacts_with
Interactions.ttl
prot:Q9VGZ4
prot:P25724

RDF data model is simple
• Everything can be said with triples 
• Generic triple stores
– low maintenance data integration 
• SPARQL
– SQL
– XPath
– Regular expressions
for RDF
for RDF
for RDF

Comparison
Flat file XML RDF
Standard NO YES YES
Scalable NO YES YES +
Extendable NO NO YES
Generic 
Data model
NO NO YES

Most common failure in RDF world:
Philosophy over pragmatism
1.
Be
honest
about
your
data

• what
you
have:
not
what
you
want

2.
Change
the
concept
change
the
IRI

•
One
concept
can
be
referred
to
by
multiple

IRI

3.
Better
to
“todo”
than
to
“debate”

Model real data not the the “real world”
• Describe
records
that
relate
to
real
world

things

• Acknowledge
that
they
are
records

• Model
measurements
before
“facts”

Example: mouse in a lab
1.5g
<weight>

1.5g
<weight>
20g
<weight>

1.5g
<measurement>
20g
<measurement>
<weight>
<weight>
1week
3week
_:1
_:2
<age>
<age>

OWL: Web Ontology Language
• Will
be
presented
in
detail
during
the
week

• Logical
meaning
added
to
RDF
statements

• That
tools
use

• Classifies
existing
data
or
infers
new
data

• Very
powerful
and
useful

‹#›
DANGER
It
is
pure
Logic
(first order)

45

Classification by restricting set membership
<human> a owl:Class ;
rdfs:subClassOf [ owl:onProperty <legs> .
owl:cardinality 2 ] ;
rdfs:subClassOf [ owl:onProperty <brains> .
rdfs:subClassOf [ owl:onProperty <referenceGenome> .
owl:allValuesFrom <HGCHR_genome> ] ;
…

Classification by restricting set membership
<human> a owl:Class ;
rdfs:subClassOf [ owl:onProperty <legs> .
rdfs:subClassOf [ owl:onProperty <brains> .
rdfs:subClassOf [ owl:onProperty <referenceGenome> .
owl:allValuesFrom <HGCHR_genome> ] ;
…
Lose a leg → no longer human

W3C workgroup in progress
• Data-‐Shapes

• You
don’t
want
to
know
how
the
sausage
is

made…

• Vendors
looking
forward
to
implementing
it

• Currently
not
that
bad,
could
be
better

• First
Working
Draft

SPARQL
What?
Why?
SPARQL?
Exam
ples
Exam
ples

Why provide a public SPARQL endpoint
• A
10
man
wet
laboratory
can
not
afford:

– to
host
their
own
database
in
house
holding

all
or
even
a
bit
of
all
life
science
data.

– not
to
have
access,
and
use,
existing
life

science
information.

← Not CPU Time...
But Brain Time
↓
The right kind of optimisation

• Classical
SQL
can
be
provided
on
the
web

–Is
not
practical

–No
federation

–Poor
standards
conformance

• Local SQL is expensive
• Local
JSON
is
no
better

• Nor
is
local
XML

Data Integration Traditional
Pathway.txt
UniProt.txt
Pathway
Parser
UniProt
Parser
Pathway
Schema
UniProt
Schema
Own Lab Data
Data
warehouse
SQL
queries
$
$
$
$
$
$

Data Integration RDF/SPARQL
Pathway.rdf
UniProt.rdf
Own Lab Data
Triple Store
SPARQL
Queries
$
$?

• Document
centric
REST
is
not
enough

–Swiss-‐Prot
available
as
REST

–(over e-mail !!) since 1986
–expasy.ch since 1993
–www.uniprot.org
since
2002

• Most user use a GUI not a CLI
• developers
build
GUI
on
a
CLI

60
© 2015 SIB
help@uniprot.org

100
10'000
1'000'000
2015-012015-022015-032015-042015-052015-062015-072015-08
queries ask select
construct describe
Queries per month in 2015
peak: 4 million per month

Real users
Mix between hard analytics and super specific
Estimate somewhere between:
300 - 1000 real humans per month
We know they are real because they take
holidays ;)

Using the Semantic Web for faster (Bio-) Research

Exercises with SPARQL
tutorial.sparql.uniprot.org

Why learn SPARQL
• Standardised formal query language
– implementation independent
• SPARQL ➔ SQL (via R2RML)
• SPARQL ➔ webservice (via SADI)
• SPARQL ➔ LDAP (e.g. SquirrelRDF)
• SPARQL ➔ RDF (triplestore e.g. OWLIM-se)
• SPARQL ➔ HADOOP/HIVE (e.g. SHARD)
• SPARQL ➔ Linked Data Fragments
– How you query independent of how you store!

Apparently it helps
kill vampires !!!

Lets look at an single taxon record
www.uniprot.org/taxonomy/9993

@base <http://purl.uniprot.org/taxonomy/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix up: <http://purl.uniprot.org/core/> .
<9993> rdf:type up:Taxon ;
up:rank up:Species ;
up:reviewed true ;
up:mnemonic "MARMR" ;
up:scientificName "Marmota marmota" ;
up:commonName "Alpine marmot" ;
up:otherName "European marmot" ;
rdfs:seeAlso <http://animaldiversity.ummz.umich.edu/site/
accounts/information/Marmota_marmota.html> ,
<http://www.alphagalileo.org/Organisations/ViewItem.aspx?
OrganisationId=2043&ItemId=70106&CultureCode=en> ,
<http://www.arkive.org/alpine-marmot/marmota-marmota/
info.html> ,

Turtle is the RDF serialization aligned with
SPARQL
• Shorthand
to
avoid
typing
so
much

– .
‘dot’
is
end
statement

– ;
‘semi-‐colon’
repeat
subject

– ,
‘comma’
is
repeat
subject
and
predicate

• prefix

– before
‘:’
is
abbreviation
of
uri

Why don’t these queries work elsewhere?
• PREFIX

– On
the
web
you
often
have
to
add
these

– But
some
can
be
preconfigured
PREFIX :<http://purl.uniprot.org/core/>
SELECT ?x
FROM <http://purl.uniprot.org/taxonomy/>
WHERE {?x a :Taxon}

a = rdf:type = <http://www.w3.org/1999/02/22-
rdf-syntax-ns#type>

up:reviewed true ;
rdfs:subClassOf <9992> ;
skos:narrowerTransitive <9994> ;
rdfs:subClassOf
taxon:9994 is a more specific
classification than

up:reviewed true ;
rdfs:subClassOf <9992> ;
skos:narrowerTransitive <9994> ;
rank => “The level, for nomenclatural
purposes, of a taxon in a taxonomic
hierarchy”

Lets learn SPARQL
• Queries
over
RDF
data.

– Four
basic
types

• SELECT

– Returns
“tab
delimited”
results

• CONSTRUCT

– Makes
new
triples

• DESCRIBE

– Returns
all
triples
mentioning
a

resource

SPARQL:queries triple pattern
taxon:9606 rdf:type core:Taxon .

?anyTaxon rdf:type core:Taxon .

SELECT ?anyTaxon
WHERE {
}

taxon:9606 rdf:type core:Taxon .
taxon:9606 core:reviewed “true” .

?anyTaxon core:reviewed “true” .

?anyTaxon core:reviewed “true” .
SELECT ?anyTaxon
WHERE {
}

?anyTaxin core:reviewed “true” .
SELECT ?anyTaxon
WHERE {
}

$anyTaxon core:reviewed “true” .
SELECT ?anyTaxon
WHERE {
}

1: Select all taxon from NCBI/UniProt taxonomy
• Taxonomy
at
www|sparql.uniprot.org

• Matches
NCBI

• Time
sync

• Adds
more
names

• Ands
images

Lets learn SPARQL
Shorthand a = rdf:type

4: Two variables one output column

5: Optional
• When
values
may
be
missing

– yet
interesting
when
they
are
there

• Use
as
sub
query

• bound
values
from
outside
stay
bound

inside

– ?x
?y?z
.
OPTIONAL
{?x
?b
?c}

• ?x
same
variable
=
same
thing

6: UNION
• Allows
you
to
combine
query
patterns
as
an

OR
operation.

• Joins
are
still
from
outer
to
inner.

Negation
• When
you
do
not
want
a
certain
category
of

matches.
SELECT ?pet
WHERE {
?pet a pets:Friendly .
}

MINUS{} or FILTER (NOT EXISTS{})
• Whats
the
difference?

– MINUS
subtracts
results

– NOT
EXITS
tests
if
the
sub
pattern
is

possible
at
all.

• Normally
the
faster
option.

10: FILTER (NOT EXISTS{}) no results

11: Negation option 3
SPARQL 1.0
SELECT ?subject ?rank
WHERE {
?subject up:rank ?rank .
OPTIONAL { ?subject up:rank up:Genus .
?subject up:rank ?genus .}
FILTER(! BOUND(?genus))
}

FILTERS
• You
just
saw
it
twice

– Once
in
the
!BOUND

– Once
in
the
NOT
EXISTS

• FILTERS
a
result
set
by
possibly
removing

values

– FILTER
do
not
add
a
value
to
the
result

• Inside
the
same
graph
pattern
order

independent.

Using implicit AND between lines

FILTER on numbers
• <

– FILTER
(1
<
2)

(17)

• >

– FILTER
(2
>
1)

(18)

• =

– FILTER
(1
=1)

(19)

• !=

– FILTER(1
!=
2)

(20)

Filters
• ?x
=
?y
does
casting
(value
conversions)
(21)

– 1.0^^xsd:float
=
1^^xsd:int
is
true

• sameTerm(?x,
?y)
does
not
(22)

– sameTerm(1.0^^xsd:float,
1^^xsd:int)

FUNCTIONS for in filters and in binds
• Functions

– STRLEN

– SUBSTR

– UCASE

– LCASE

– STRSTARTS

– STRENDS

– CONTAINS

– STRBEFORE

– STRAFTER

– ENCODE_FOR_URI

– CONCAT

– langMatches

– REGEX

– REPLACE

– IRI

– STR

25: CONTAINS is case sensitive is it in
there

26: REGEX, just like java|python regex

BIND
• Builds
new
Values

– Closes
the
basic
graph
pattern
(22)

• Always
declare
before
use.
SELECT ?p WHERE {
{
?taxon a :Taxon .
}
BIND (?taxon AS ?p)
}

BIND existing variable to a new one

Aggregate functions
• on
select
line

• limited
in
number

– count

– sum

– avg

– min

– max

– groupConcat

– sample

33: Finding a grand parent using normal
joins

34: Finding a grandParent using a path
query

38: Can use the variable in a normal join
afterwards

GROUP BY
• Needed
for
aggregate
values

• After
closing
the
where
clause

– ...
WHERE
{?x
?y
?z}
GROUP
BY
?x

HAVING
• FILTER
for
aggregates

• After
the
GROUP
BY
clause

– ...
GROUP
BY
?x
HAVING
(count(?y)
>
2)

– ...
GROUP
BY
?x
HAVING
(min(?y)
=
2)

– etc...

41: LIMIT and OFFSET
• OFFSET
is
skip
first
results

• LIMIT
return
no
more
than
x
results

VALUES
• Super
BIND

• Provide
inline
data

Examples
• Parameter
lists
are
between
()

Text
VALUES (?annotation) {
(core:Disease_Annotation)
(core:Disulfide_Bond_Annotation)
}

Examples
• Undef
means
no
value
at

– all
not
bound
Text
VALUES (?annotation ?begin) {
(core:Disease_Annotation UNDEF)
(core:Disulfide_Bond_Annotation 2)
}

VALUES
• After
declaring
a
set
of
values
you
can
use

them
in
your
query.
SELECT ?comment WHERE {
VALUES (?annotation ?begin) {
(core:Disease_Annotation UNDEF)
(core:Disulfide_Bond_Annotation 2)
}
?annotation rdfs:comment ?comment .
}

SERVICE: Using other sparql endpoints
• SERVICE<URL
of
other
endpoint>

– Runs
a
sub
query
on
the
other
endpoint

and
merges
it
back
into
your
query.

“Life is better with friends who understand you.”

SERVICE
• Useful

– Quick
experimenting
with
combing
multiple

datasources

– Quick
for
queries
where
not
to
much
data
is
send

to
the
remote
point

• Slow

– When
you
ask
for
to
much
data

– Remote
endpoint
not
resourced
for
your

questions

SERVICE
• Slowly
improving

• Theoretically
unfixable

• Practically
could
be
much
better

• 1000
x
speed
up
small
step
away

Construction
• CONSTRUCT

– New
triples

• downloads
RDF

– Does
not
update
store

Constructing an owl:sameAs between two
URI

INSERT
• Adds
data

– like
construct

DELETE
• Removes
data

– Triples
matching
are
removed
from
the

data

– Triples
can
be
bound
using
where
clause

DELETE
INSERT
• Single
atomic
operation

• Transactions
store
API
option

Of Course Biology is complicated
#baseURI: http://purl.uniprot.org/unirule/UR000107224

#Rule UR000107224 Created by:bridge on:2009-02-12 Modiﬁed by:rantunes on:2015-06-09

PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>

PREFIX uniprot:<http://purl.uniprot.org/uniprot/>

PREFIX sequence:<http://purl.uniprot.org/sequences/>

PREFIX unirule:<http://purl.uniprot.org/unirules/>

PREFIX taxon:<http://purl.uniprot.org/taxonomy/>

PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>

PREFIX hamap-sparql:<http://example.org/hamap_sparql/>

PREFIX up:<http://purl.uniprot.org/core/>

PREFIX faldo:<http://biohackathon.org/resource/faldo#>

PREFIX method:<http://example.org/method/>

PREFIX keyword:<http://purl.uniprot.org/keywords/>

PREFIX owl:<http://www.w3.org/2002/07/owl#>

PREFIX proteome:<http://purl.uniprot.org/proteomes/>

PREFIX hamap:<http://purl.uniprot.org/hamap/>

PREFIX annotation:<http://purl.uniprot.org/annotation/>

PREFIX xsd:<http://www.w3.org/2001/XMLSchema#>

CONSTRUCT {

?this up:annotation ?annotation0,

?annotation1,

?annotation2,

?annotation3,

?annotation5;

up:classiﬁedWith <http://purl.obolibrary.org/obo/19805>,

<http://purl.obolibrary.org/obo/334>,





keyword:223,

keyword:560,

keyword:662 .

?annotation0 a up:Function_Annotation;

rdfs:comment "Catalyzes the oxidative ring opening of 3-hydroxyanthranilate to 2-amino-3-carboxymuconate semialdehyde, which

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