RDF what is it and why should we use it in the life sciences. Followed by an introduction to SPARQL in detail.

1. RDF what and why
Jerven Bolleman
Developer
Swiss-Prot Group

2. Introduction
• RDF
• Its
a
technology
• Cost
and
affordability
are
key
concerns

4. ]<--.+++++++++++.++++++++.---------.>++++++++[<---------->-]
<++.>+++++[<+++++++++++++>-]<.+++++++++++++.----------.>++++
+++[<---------->-]<++.>++++++++[<++++++++++>-]<.>+++[<----->
-]<.>+++[<++++++>-]<..>+++++++++[<--------->-]<--.>+++++++[<
++++++++++>-]<+++.+++++++++++.>++++++++[<----------->-]<++++
.>+++++[<+++++++++++++>-]<.>+++[<++++++>-]<-.---.++++++.----
---.----------.>++++++++[<----------->-]<+.---.[-]<<<->[-]>[
-]<<[>+>+<<-]>>[<<+>>-]>>>[-]<<<+++++++++<[>>>+<<[>+>[-]<<-]
>[<+>-]>[<<++++++++++>>>+<-]<<-<-]+++++++++>[<->-]>>+>[<[-]<
<+>>>-]>[-]+<<[>+>-<<-]<<<[>>+>+<<<-]>>>[<<<+>>>-]<>>[<+>-]<
<-[>[-]<[-]]>>+<[>[-]<-]<++++++++[<++++++<++++++>>-]>>>[>+>+
<<-]>>[<<+>>-]<[<<<<<.>>>>>-]<<<<<<.>>[-]>[-]++++[<++++++++>
-]<.>++++[<++++++++>-]<++.>+++++[<+++++++++>-]<.><+++++..---
-----.-------.>>[>>+>+<<<-]>>>[<<<+>>>-]<[<<<<++++++++++++++
.>>>>-]<<<<[-]>++++[<++++++++>-]<.>+++++++++[<+++++++++>-]<-
-.---------.>+++++++[<---------->-]<.>++++++[<+++++++++++>-]
<.+++..+++++++++++++.>++++++++[<---------->-]<--.>+++++++++[
<+++++++++>-]<--.-.>++++++++[<---------->-]<++.>++++++++[<++
++++++++>-]<++++.------------.---.>+++++++[<---------->-]<+.
>++++++++[<+++++++++++>-]<-.>++[<----------->-]<.+++++++++++
..>+++++++++[<---------->-]<-----.---.+++.---.[-]<<<]
@

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

6. 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

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

8. Related triples form a graph...

9. 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

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

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

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

13. 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”?

14. Hello, I a 9993.
I like flower?

15. 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!

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

17. 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

18. 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
purl.uniprot.org/uniprot/P32234
purl.uniprot.org/uniprot/P32234
...prot/P42643
...prot/P42643
...prot/P42643
...prot/P41932
...prot/P41932
...prot/P41932
...prot/P41932

19. 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

20. Example step 3: Make statements
A Triple
Interactions.txt
@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
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 .

21. Example step 4: Use URIs for properties
@prefix prot:<purl.uniprot.org/uniprot/>
@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 .

22. 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

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

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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

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

32. 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!

33. 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 prot:<purl.uniprot.org/uniprot/>
@prefix intact:<fake.ebi.ac.uk/intact/example>
prot:P32234
prot:P32234
intact:interacts_with
intact:interacts_with
Interactions.ttl
prot:Q9VGZ4
prot:P25724

34. 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

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

36. Modeling data using RDF

37. 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”

38. 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”

39. Example: mouse in a lab
1.5g
<weight>

40. Example: mouse in a lab
1.5g
<weight>
20g
<weight>

41. TIME it made you a liar

42. Example: mouse in a lab
1.5g
<measurement>
20g
<measurement>
<weight>
<weight>
1week
3week
_:1
_:2
<age>
<age>

43. Describing models using
OWL

44. 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

45. ‹#›
DANGER
It
is
pure
Logic
(first order)
45

46. Classification by restricting set membership
<human> a owl:Class ;
rdfs:subClassOf [ owl:onProperty <legs> .
owl:cardinality 2 ] ;
rdfs:subClassOf [ owl:onProperty <brains> .
owl:cardinality 1 ] ;
rdfs:subClassOf [ owl:onProperty <referenceGenome> .
owl:allValuesFrom <HGCHR_genome> ] ;
…

47. Classification by restricting set membership
<human> a owl:Class ;
rdfs:subClassOf [ owl:onProperty <legs> .
owl:cardinality 2 ] ;
rdfs:subClassOf [ owl:onProperty <brains> .
owl:cardinality 1 ] ;
rdfs:subClassOf [ owl:onProperty <referenceGenome> .
owl:allValuesFrom <HGCHR_genome> ] ;
…
Lose a leg → no longer human

48. Validating RDF Data

49. 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

50. SPARQL
What?
Why?
SPARQL?
Exam
ples
Exam
ples

51. 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.

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

53. Why provide a public SPARQL endpoint
• 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

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

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

56. Why provide a public SPARQL endpoint
• 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

57. 57
© 2015 SIB

58. 58
© 2015 SIB

60. 60
© 2015 SIB
help@uniprot.org

61. 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

62. 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 ;)

63. Using the Semantic Web for faster (Bio-) Research

64. Exercises with SPARQL
tutorial.sparql.uniprot.org

65. 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!

66. Apparently it helps
kill vampires !!!

67. Its SPARQLy mammal time !!

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

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

71. @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> ,

72. 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

73. 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}

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

75. <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:subClassOf <9992> ;
skos:narrowerTransitive <9994> ;
rdfs:subClassOf
taxon:9994 is a more specific
classification than

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

77. 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

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

79. SPARQL:queries triple pattern
?anyTaxon rdf:type core:Taxon .

80. SPARQL:queries triple pattern
?anyTaxon rdf:type core:Taxon .
SELECT ?anyTaxon
WHERE {
}

81. SPARQL:queries triple pattern
taxon:9606 rdf:type core:Taxon .
taxon:9606 core:reviewed “true” .

82. SPARQL:queries triple pattern
?anyTaxon rdf:type core:Taxon .
?anyTaxon core:reviewed “true” .

83. SPARQL:queries triple pattern
?anyTaxon rdf:type core:Taxon .
?anyTaxon core:reviewed “true” .
SELECT ?anyTaxon
WHERE {
}

84. SPARQL:queries triple pattern
?anyTaxon rdf:type core:Taxon .
?anyTaxin core:reviewed “true” .
SELECT ?anyTaxon
WHERE {
}

85. SPARQL:queries triple pattern
?anyTaxon rdf:type core:Taxon .
$anyTaxon core:reviewed “true” .
SELECT ?anyTaxon
WHERE {
}

86. tutorial.sparql.uniprot.org

87. 1: Select all taxon from NCBI/UniProt taxonomy
• Taxonomy
at
www|sparql.uniprot.org
• Matches
NCBI
• Time
sync
• Adds
more
names
• Ands
images

88. ‹#›
88

89. Lets learn SPARQL
Shorthand a = rdf:type

90. 2: AND join (default)

91. 3: Shortcuts

92. Remember ‘;’ shortcut

93. 4: Two variables one output column

94. 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

95. 5: OPTIONAL commonName

96. 6: UNION
• Allows
you
to
combine
query
patterns
as
an
OR
operation.
• Joins
are
still
from
outer
to
inner.

97. UNION

98. Negation
• When
you
do
not
want
a
certain
category
of
matches.
SELECT ?pet
WHERE {
?pet a pets:Friendly .
}

99. Oooops

100. 7: Not exists (Negation 1)

101. 8: Minus (Negation 2)

102. 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.

103. 9: MINUS all data

104. 10: FILTER (NOT EXISTS{}) no results

105. 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))
}

107. 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.

108. 12: Filter

109. 13: Filter on not in

110. Using implicit AND between lines

111. Using implicit AND between lines

112. 15: FILTER IN

113. 16: FILTER using OR

114. FILTER on numbers
• <
– FILTER
(1
<
2)
(17)
• >
– FILTER
(2
>
1)
(18)
• =
– FILTER
(1
=1)
(19)
• !=
– FILTER(1
!=
2)
(20)

115. 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)

116. 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

117. 24: SUBSTR == substring

118. 24: STRLEN == String Length

119. 25: CONTAINS is case sensitive is it in
there

120. 26: REGEX, just like java|python regex

121. BIND
• Builds
new
Values
– Closes
the
basic
graph
pattern
(22)
• Always
declare
before
use.
SELECT ?p WHERE {
{
?taxon a :Taxon .
}
BIND (?taxon AS ?p)
}

122. BIND existing variable to a new one

124. 27: CONCAT

125. BIND can assign any output

126. Aggregate functions
• on
select
line
• limited
in
number
– count
– sum
– avg
– min
– max
– groupConcat
– sample

127. © 2013 SIB
30: count

128. © 2013 SIB
31: SAMPLE should give a random result back

129. © 2013 SIB
Follow the path

130. 32: Path queries

131. 33: Finding a grand parent using normal
joins

132. 34: Finding a grandParent using a path
query

133. 35: | is OR for predicate

134. 36: Same result with UNION

135. 37: Finding any ancestor

136. 38: Can use the variable in a normal join
afterwards

137. © 2013 SIB
GROUP BY

138. GROUP BY
• Needed
for
aggregate
values
• After
closing
the
where
clause
– ...
WHERE
{?x
?y
?z}
GROUP
BY
?x

139. 39: GROUP BY

140. HAVING
•
I have carrot !

141. HAVING
• FILTER
for
aggregates
• After
the
GROUP
BY
clause
– ...
GROUP
BY
?x
HAVING
(count(?y)
>
2)
– ...
GROUP
BY
?x
HAVING
(min(?y)
=
2)
– etc...

142. 40: HAVING

143. © 2013 SIB
LIMITS
&
OFFSET

144. 41: LIMIT and OFFSET
• OFFSET
is
skip
first
results
• LIMIT
return
no
more
than
x
results

145. ORDER

146. ORDER

147. © 2013 SIB

149. VALUES
• Super
BIND
• Provide
inline
data

150. Marmota marmota marmota

151. Examples
• Parameter
lists
are
between
()
Text
VALUES (?annotation) {
(core:Disease_Annotation)
(core:Disulfide_Bond_Annotation)
}

152. Examples
• Undef
means
no
value
at
– all
not
bound
Text
VALUES (?annotation ?begin) {
(core:Disease_Annotation UNDEF)
(core:Disulfide_Bond_Annotation 2)
}

153. 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 .
}

154. 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.

155. “Life is better with friends who understand you.”

156. SERVICE

157. 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

158. SERVICE
• Slowly
improving
• Theoretically
unfixable
• Practically
could
be
much
better
• 1000
x
speed
up
small
step
away

159. Lets make
some triples

160. Construction
• CONSTRUCT
– New
triples
• downloads
RDF
– Does
not
update
store

162. Constructing an owl:sameAs between two
URI

163. INSERT
• Adds
data
– like
construct

165. DELETE
• Removes
data
– Triples
matching
are
removed
from
the
data
– Triples
can
be
bound
using
where
clause

166. DELETE

167. DELETE
INSERT
• Single
atomic
operation
• Transactions
store
API
option

168. Atomic operation

169. © 2013 SIB
I’m exhausted now

170. 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>,
<http://purl.obolibrary.org/obo/34354>,
<http://purl.obolibrary.org/obo/43420>,
<http://purl.obolibrary.org/obo/6569>,
<http://purl.obolibrary.org/obo/8198>,
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

171. Questions