Semantic Web for Life Sciences: vision, aims, tools, platforms

Semantic Web for Life
Sciences
Visions, Aims, Tools, Platforms
Andrea Splendiani
Semantic Web for Life Sciences:Visions, Aims, Tools, Platforms
mercoledì 16 ottobre 13

Nettab, 16th October 2013,Venice

Andrea Splendiani @AndSplend
andrea.splendiani@[deri.org,intellileaf.com]

Index




An Introduction to Semantic Web Technologies




What

A new form of Web
content that is meaningful
to computers will unleash
a revolution of new
possibilities

Web + Semantics



Quick Intro to RDF

•

RDF: Resource Description
Framework

•

A language to represent
concepts and their relations

•

Simple statements:
Subject Predicate Object

•

Conceptually a graph




Quick Intro to RDF

•

RDF: Resource Description
Framework

•

A language to represent
concepts and their relations

•

object
predicate
subject

Simple statements:
Subject Predicate Object

•

Conceptually a graph




Quick Intro to RDF

•

How does this work in
practice ?

•

We identify each node and
edge by a URI

•

(we only need to identify, not
deﬁne things!)

•

object
predicate
subject

URIs could be Webresolvable

http://nettab.org/biology/Chromosome http://nettab.org/properties/contains http://nettab.org/biology/DNA



Quick Intro to RDF

http://nettab.org/biology/Chromosome http://nettab.org/properties/contains http://nettab.org/biology/DNA




Quick Intro to RDF: RDF vs other modeling

RDF:

•
•
•

No data-types
No cardinalities
Importance of identiﬁcation



Quick Intro to RDF: RDF vs other modeling

The Semantic Web is easy
(a bit like html)



Technology details

•
•
•
•
•
•
•
•
•
•

URIs
The technology stack
Serializations
Blank nodes
Named graphs
Semantics
Computational representation of meaning
RDFS
OWL
SKOS




Technology details: URIs

http://nettab.org/biology/Chromosome

•
•

URIs are global identiﬁers

•

Simple but implicit authority mechanism
(namespace)

Supported by a widely adopted infrastructure
(the Web)




http://www.ncbi.nlm.nih.gov/pubmed/20605693
http://dbpedia.org/resource/Berlin
http://biocyc.org/biopax/biopax#smallMolecule84998
http://www.reactome.org/biopax#H2O__ChEBI_15377_
http://bio2rdf.org/go:0032283

•
•
•

Not a consistent “scheme” to deﬁne URIs
Good practices: e.g.: no parameters
Decision points: semantically opaque or not





•
•
•

What is necessary:
Unambiguous identiﬁcation
Persistence (versions)

http://nettab.org/Venice




What should URIs resolve to ?

Nothing

http://wifo5-03.informatik.uni-mannheim.de/bizer/pub/LinkedDataTutorial/



Technology details: the technology stack

Not a coherent monolithic technology!



Technology details: serializations
RDF can be represented
in many syntaxes

•
•
•
•
•
•

N-Triples

English

programmed cell death

http://www.w3.org/2000/01/rdf-schema#label
http://purl.org/go/owl#GO_0012501

Turtle
XML(s)
...
JSON
Binary

http://www.w3.org/2000/01/rdf-schema#subClassOf


110020
http://www.w3.org/1999/02/22-rdf-syntax-ns#type
http://www.example.com/hasdbaccession

Integer

http://www.example.com/
instance1

Use a tool like Rapper to convert among syntaxes
http://librdf.org/raptor/



N-Triples
<http://purl.org/obo/owl/GO#GO_0006915> <http://www.w3.org/2000/01/rdf-schema#subClassOf> <http://purl.org/obo/owl/GO#GO_0012501> .
<http://purl.org/obo/owl/GO#GO_0012501> <http://www.w3.org/2000/01/rdf-schema#label> "programmed cell death"@en .
<http://www.example.com/instance1> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/obo/owl/GO#GO_0006915> .
<http://www.example.com/instance1> <http://www.example.com/hasdbaccession> "110020"^^<http://www.w3.org/2001/XMLSchema#int> .
English


•
•
•


Every subset (lines) is valid N-Triples
Good for large ﬁles, streaming,...
Very simple to produce via scripts




110020
instance1




Integer

Turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix GO: <http://purl.org/obo/owl/GO#> .
@prefix : <http://www.example.com/> .

http://preﬁx.cc/

go:GO_0006915
rdfs:subClassOf go:GO_0012501 .
go:GO_0012501>
rdfs:label "programmed cell death"@en .
:instance1
:hasdbaccession "110020"^^<http://www.w3.org/2001/XMLSchema#int> ;
a go:GO_0006915 .

English




•
•


Easy to read


Easy to write by hand (keyboard)


110020
instance1



Integer

RDFXML
<?xml version="1.0" encoding="utf-8"?>
<rdf:RDF xmlns:go="http://purl.org/obo/owl/GO#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://www.example.com/">
<rdf:Description rdf:about="http://purl.org/obo/owl/GO#GO_0006915">
<rdfs:subClassOf rdf:resource="http://purl.org/obo/owl/GO#GO_0012501"/>
</rdf:Description>
<rdf:Description rdf:about="http://purl.org/obo/owl/GO#GO_0012501">
<rdfs:label xml:lang="en">programmed cell death</rdfs:label>
</rdf:Description>
<rdf:Description rdf:about="http://www.example.com/instance1">
<rdf:type rdf:resource="http://purl.org/obo/owl/GO#GO_0006915"/>
</rdf:Description>
<rdf:Description rdf:about="http://www.example.com/instance1">
<hasdbaccession rdf:datatype="http://www.w3.org/2001/XMLSchema#int">110020</hasdbaccession>
</rdf:Description>
</rdf:RDF>
<go:GO_0006915 rdf:about="http://www.example.com/instance1">
<hasdbaccession rdf:datatype="http://www.w3.org/2001/XMLSchema#int">110020 </hasdbaccession>
</go:GO_0006915>

Good for ?



Technology details: blank nodes

•

Some entities may not have a
global identiﬁer

•

Could we always provide
one ?
<rdf:Description rdf:about="http://
dbpedia.org/resource/Venice">
<foaf:based_near>
<geo:Point>
<geo:lat>45.4375</geo:lat>
<geo:long>12.335833</geo:long>
</geo:Point>
</foaf:based_near>
</rdf:Description>

<http://dbpedia.org/resource/Venice>
foaf:based_near [
geo:lat "45.4375" ;
geo:long "12.335833" ;
a <http://example/Point>
].
<http://dbpedia.org/resource/Venice> foaf:based_near _:p1 .
_:p1 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://example/Point> .
_:p1 <http://www.w3.org/2003/01/geo/wgs84_pos#:lat> "45.4375" .
_:p1 <http://www.w3.org/2003/01/geo/wgs84_pos#:long> "12.335833" .



Technology details: named graphs

•

An entire (sub)graph can be
identiﬁed (“named”) by a URI

•

Graphs can be subject and object of
predicates

•

Good to represent context (hence
provenance...)

<http://dbpedia.org/resource/Venice> foaf:based_near _:p1 nettabex:1.
_:p1 rdf:type <http://example/Point> nettabex:1.
_:p1 geo:lat "45.4375" nettabex:1.
(pseudo
_:p1 geo:long "12.335833" nettabex:1.


N-quads)


Technology details: semantics

Facts

Deﬁning reality

Computing deﬁnitions

Ontology:
“A formalization of a conceptualization”



Technology details: semantics
Ontologies:
The conceptualization
extract from BFO

http://www.ifomis.org/bfo

extract from RO



Technology details: representation of meaning
Computational representation of meaning:
transitive property
programmed cell
death

programmed cell
death

rdfs:label

rdfs:label

GO:GO_0012501

GO:GO_0012501

apoptosis

apoptosis

rdfs:subClassOf

rdfs:subClassOf

rdfs:label

rdfs:label

GO:GO_0006915

GO:GO_0006915
virus-infected
cell
apoptosis

rdfs:subClassOf rdfs:label

virus-infected
cell
apoptosis

GO:GO_0008585

GO:GO_0008585

(?x rdfs:subClassOf ?y)(?y rdfs:subClassOf ?z) ->
(?x rdfs:subClassOf ?z)



Some possible computations

• Inconsistency
• Classiﬁcation
• Consistency
• Subsumption





• Emergent Semantics
• First order logic
• Inference rules
• RDFS
• OWL (-2, DL, RL, QL...)
• SKOS




Technology details: RDFS

programmed cell
death

rdfs:label

RDF-S:
• type
• subClassOf
• subPropertyOf
• ...

GO:GO_0012501
apoptosis

rdfs:subClassOf

rdfs:label

GO:GO_0006915
virus-infected
cell
apoptosis

GO:GO_0008585

Inconsistence ?



Technology details: OWL

OWL:
• Class Union/Intersection
• Universal/Existential
restriction
• Property Domain/Range
• Transitive/Reﬂexive/
Functional properties
• ...


OWL provides a set of properties and
resources to characterize classes.

•

OWL supports inference of the class structure
of an ontology

•

OWL has several variants addressing different
trade-offs in computability/expressivity

•

Many ontologies “use OWL”, but they really only
use a few axioms (almost like RDFS)

•
Inconsistence !

•

OWL is not a schema language! (Classes and
properties have a different meaning than OO)



Example:
cell motility part of localization of cell
<owl:Class rdf:about="GO#GO_0006928">
<rdfs:label xml:lang="en">cell motility</rdfs:label>
<rdfs:subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="obo#part_of"/>
<owl:someValuesFrom rdf:resource="GO#GO_0051674"/>
</owl:Restriction>
</rdfs:subClassOf>
...
</owl:Class>




OWL Inference is not validation!

Person hasFather exactly 1
George a Person
George hasFather Mark
George hasFather John
John sameAs Mark

•RDF Validation: Syntax
•(OWL) Reasoning: Consistency
•SPIN Rules (later)




Two things to possibly avoid when working
with OWL/RDF:

• use RDF/XML
• use OO tools



Technology details: SKOS

•

SKOS: Simple Knowledge
Organization System

•

Not a formal system (can be
mapped to OWL)

•

Weak semantic connotation,
but useful in many
classiﬁcation tasks

You don’t improve the ontological commitment of a classiﬁcation system by
representing it in OWL (if the conceptualization doesn’t improve!)



Why Life Sciences ?

Why Semantic Web ?



Why ? Heterogeneous information




Why ? Mutability of information (schema)
The textbook tale

•

The DNA model has
around 60 years (’53)

•

“basic principles” changed in
a timescale of about 10
years

Missing: epigenetics, miRNA



Why ? Complex Semantics

The meaning of “Gene”



Why ? The Semantic Web can Help How ?

•
•

Makes data integration easier
Makes semantic problems explicit




Easy data integration: what if we used a relational model

CityID

population

CityName

INT

INT

VARHCHAR
(45)

INT

INT

Date

INT

TEXT

1

40885

Venezia

1

20000

1868

2

Venice


UniversityID students established CityID


CityName



CityID

population

CityName


CityName

INT

INT

VARHCHAR
(45)

INT

INT

Date

INT

TEXT

1

40885

Venezia

1

20000

1868

2

Venice

Implicit foreign key
In the Semantic Web “keys” are explicit by deﬁnition




CityID

population

CityName


CityName

INT

INT

VARHCHAR
(45)

INT

INT

Date

INT

TEXT

1

40885

Venezia

1

20000

1868

2

Venice

Local identiﬁers, mappings required



Perhaps we can assume that in the same
country, two cities with the same name
are the same city.
We could just declare rdfs:property as
functional to deduce such equivalences.
CityID

population

CityName


CityName

INT

INT

VARHCHAR
(45)

INT

INT

Date

INT

TEXT

1

40885

Venezia

1

20000

1868

2

Venice

What is the type of a CityName ?
How many names can a city have ? Is the city name a key ?
Does the University of Venice only exists in Venice ?



The broader view




The broader view: Not only NoSQL
The Semantic Web is not only a technology to handle data.
Being “Semantic” and “Web” has some implication on the way
information is thought

•
•
•

All predicates are global (no implicit contexts)

•
•

Monotonicity: you only add information

Declarative vs imperative processing of information
Immutability: once information is out, you cannot
control it
There may be more that what you know: Open
World Assumption




HOWTO publish information on the Semantic Web





Simply link an RDF ﬁle
Possibly via a good URI



Programmatic access via a SPARQL endpoint
(can be resource intensive)




Embed RDF in html (e.g.: RDFa)
<div xmlns:dc="http://purl.org/dc/elements/1.1/">

<div about="/alice/posts/trouble_with_bob">
<h2 property="dc:title">The trouble with Bob</h2>
<h3 property="dc:creator">Alice</h3>
...
</div>
<div about="/alice/posts/jos_barbecue">
<h2 property="dc:title">Jo's Barbecue</h2>
<h3 property="dc:creator">Eve</h3>
...
</div>
</div>




Publish information as Linked-Data

•
•
•

Linked Data is a set of good practices to publish RDF

•

Content negotiation

Use only HTTP resolvable URIs
Dereferencing URIs (202 information resources 303
see other)

http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/
Linked Data APIs (ELDA), Linked Data Platform, Linked Data
JSON ...



HOWTO: simple conversions to RDF

CityID

population

CityName

INT

INT

VARHCHAR(45)

1

40885

Venice

LAT pp
LAT pp
LAT pp
LAT pp
LAT pp
SLP-76
SLP-76
SLP-76
SLP-76
SLP-76
SLP-76
SLP-76
SLP-76
CD28
CD28
CD28

Grb2
PI3K_p85-alpha
PLC-gamma
Grap
Gads
pp Vav
pp Nck
pp SLAP-130
pp Gads
pp Shc
pp Cbl
pp Lyn
pp PLC-gamma2
pp Grb2
pp PI3K_p85-beta
pp CD80

SIF

Table
http://ns/ID#1 http://ns/prop#population 40885
http://ns/ID#1 http://ns/prop#CityName Venice

<http://ns/ID#$1 http://ns/prop#$2 http://ns/ID#$3>




HOWTO: relational to RDF mapping
D2RQ

•
•
•

Relational to RDF conversion
Dynamic rendering (SPARQL)
Linked data frontend (Pubby)

http://d2rq.org

http://www.w3.org/TR/2012/PR-rdb-direct-mapping-20120814/
http://www.w3.org/TR/2012/REC-r2rml-20120927/




Author

Paper

ID (key)

ID (key)

Name

PubmedID

Email

Title

AuthorID (fkey)
PaperID (fkey)

Automated mapping:
D2RQ (like other tools) by default
only generates a structural
mapping (this can then be tuned).
It cannot generate a dc:creator
property between Paper and
Author

...

Author2Paper





Author

Paper

ID (key)

ID (key)

Name

PubmedID

Email

Title

AuthorID (fkey)
PaperID (fkey)
...

Author2Paper


Example edited mapping:
# Table Author
map:author a d2rq:ClassMap;
d2rq:dataStorage map:database;
d2rq:uriPattern "author/@@Author.ID@@";
d2rq:class foaf:person;
d2rq:classDefinitionLabel "author";
.
map:author_name a d2rq:PropertyBridge;
d2rq:belongsToClassMap map:author;
d2rq:property foaf:name;
d2rq:propertyDefinitionLabel "Author_name";
d2rq:column "Author.Name";
.




Author

Paper

ID (key)

ID (key)

Name

PubmedID

Email

Title

AuthorID (fkey)

map:hasAuthor a d2rq:PropertyBridge;
d2rq:belongsToClassMap map:Paper;
d2rq:property :hasAuthorEmail;
d2rq:column "Author.Email";
d2rq:join "Paper.ID <= Author2Paper.PaperID";
d2rq:join "Author2Paper.PersonID => Person.ID";
d2rq:datatype xsd:string;
d2rq:propertyDefinitionLabel "email"@en;
d2rq:propertyDefinitionComment "Author email"@en;
.

PaperID (fkey)
...

map:Paper a d2rq:ClassMap;
d2rq:class :Paper;
d2rq:uriPattern

Author2Paper

"http://togows.dbcls.jp/entry/pubmed/@@Paper.PubmedID@@.ttl

d2rq:condition "Papers.PubmedID != ””";
d2rq:dataStorage map:Database1.



HOWTO setup your own triplestore

Discussion +
Jena

http://www.apache.org/dist/jena/binaries/
Just run: fuseki --loc=path --update /



Ontologies and Protege
Ontology editor
Practical suggestions:

•
•
•

Produce RDF (simple scripts or mapper)
Translate among syntaxes via Rapper
Build ontologies (for your RDF) in Protege

http://protege.stanford.edu

Two typical scenarios of interoperability between RDF and
Ontologies
Ontologies are large
taxonomies or DAGs, only
transitive inference is used.
(E.g.: SKOS-like)

Ontologies are complex
terminology that rely on
axioms for consistency and
inference

Protege to edit and visualize
the ontology.
Rely on the triplestore for
inference.

Edit in Protege, classify the
ontology via reasoner, then
use the results in
combination with other
RDF statements.

Third scenario....




You can specify
URIs as to match
your RDF



• Relations are ﬁrst class
objects
• Specifying domain,
range and annotation
can go a long way in
• “documenting” the
ontology




VOID and metadata

VOID: Metadata about
a dataset.
• Provenance
• License
• Vocabularies
• Statistics

rdf.myexperiment.org/void.rdf
http://www.w3.org/TR/void/



VOID and metadata
SPARQL service
descriptor
• Query language
supported
• Formats returned
• Entailment regime
• ...

curl -H "Accept: application/rdf+xml"
http://beta.sparql.uniprot.org/sparql



VOID and metadata

Consider registering your data in a data register



HOWTO use information from the Semantic Web




Introduction to SPARQL
# NAME
: Get psoriasis proteins
# PARAMETER: psoriasis: the disease name
# FUNCTION : returns all the proteins that have
'psoriasis' in
#
their Swiss-Prot disease description
and their
#
interacting proteins (if known)
BASE
<http://www.semantic-systems-biology.org/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdfschema#>
PREFIX ssb:<http://www.semantic-systemsbiology.org/SSB#>
SELECT distinct ?protein_name ?disease_description
?interacts_with ?encoded_by
WHERE {
GRAPH <uniprot_sprot> {
?protein_id ssb:disease ?disease_description.
?protein_id ssb:mnemonic ?protein_name.
OPTIONAL {
?protein_id ssb:interacts_with ?interactor.
?interactor ssb:mnemonic ?interacts_with.
?interactor ssb:encoded_by ?encoded_by.
}
}
FILTER regex(?disease_description, 'psoriasis').
}

http://www.semantic-systems-biology.org/biogateway/endpoint?
default-graph-uri=&query=%23+NAME+++++%3A+Get+psoriasis+proteins
%0D%0A%23+PARAMETER%3A+psoriasis%3A+the+disease+name%0D%0A
%23+FUNCTION+%3A+returns+all+the+proteins+that+have+%27psoriasis
%27+in%0D%0A%23++++++++++++their+Swiss-Prot+disease+description
+and+their%0D%0A%23++++++++++++interacting+proteins+%28if+known
%29%0D%0A%0D%0ABASE+++%3Chttp%3A%2F%2Fwww.semantic-systemsbiology.org%2F%3E%0D%0APREFIX+rdfs%3A%3Chttp%3A%2F%2Fwww.w3.org
%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+ssb%3A%3Chttp%3A%2F
%2Fwww.semantic-systems-biology.org%2FSSB%23%3E%0D%0ASELECT
+distinct+%3Fprotein_name+%3Fdisease_description%0D%0A+++++++
%3Finteracts_with+%3Fencoded_by%0D%0AWHERE+%7B%0D%0A++GRAPH+
%3Cuniprot_sprot%3E+%7B%0D%0A++++%3Fprotein_id+ssb%3Adisease+
%3Fdisease_description.%0D%0A++++%3Fprotein_id+ssb%3Amnemonic+
%3Fprotein_name.%0D%0A++++OPTIONAL+%7B%0D%0A++++++%3Fprotein_id
+ssb%3Ainteracts_with+%3Finteractor.%0D%0A++++++%3Finteractor
+ssb%3Amnemonic+%3Finteracts_with.%0D%0A++++++%3Finteractor+ssb
%3Aencoded_by+%3Fencoded_by.%0D%0A++++%7D%0D%0A++%7D%0D%0A+
+FILTER+regex%28%3Fdisease_description%2C+%27psoriasis%27%29.%0D
%0A%7D&format=text%2Fhtml&debug=on

SPARQL is REST! (one query is one URL)



SPARQL supports different kind of queries

SELECT ?x ?y...
WHERE {GRAPH PATTERN}
CONSTRUCT {?x ?y ?z}
WHERE {GRAPH PATTERN}
ASK
DESCRIBE




Graph patterns
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX biopax3: <http://www.biopax.org/release/biopax-level3.owl#>

SELECT DISTINCT ?pathway ?pathwayname
WHERE
{?pathway rdf:type biopax3:Pathway .
?pathway biopax3:displayName ?pathwayname .
?pathway biopax3:pathwayComponent ?reaction .
?reaction rdf:type biopax3:BiochemicalReaction .
?reaction ?rel ?protein .
?protein rdf:type biopax3:Protein .
?protein biopax3:entityReference <http://purl.uniprot.org/uniprot/P01308>
}
LIMIT 100
<http://identifiers.org/reactome/REACT_15550.4> Insulin Processing
<http://identifiers.org/reactome/REACT_18325.4> Regulation of Insulin Secretion




Graph patterns




How do I know what to query ?




Filters

WHERE
FILTER regex(?pathwayname, "Insulin", "i")
}
LIMIT 100
More operators!



Combining graph matching: union and diff
WHERE
{
{?reaction ?rel ?protein .}
UNION
{
?reaction ?rel ?complex .
?complex rdf:type biopax3:Complex .
?complex ?comp ?protein .
}}
}
LIMIT 100



Aggregators
WHERE
{
{?reaction ?rel ?protein .}
UNION
{
?reaction ?rel ?complex .
?complex rdf:type biopax3:Complex .
?complex ?comp ?protein .
}}
}
LIMIT 100



Named graphs

<http://www.openlinksw.com/schemas/virtrdf#>
<http://rdf.ebi.ac.uk/dataset/reactome/45>
<http://rdf.ebi.ac.uk/dataset/reactome/description>




How do I know what to query ?
A “practical example”...
select distinct ?g where {
graph ?g {?s ?p ?o}
}

<http://www.openlinksw.com/schemas/virtrdf#>
<http://rdf.ebi.ac.uk/dataset/reactome/45>
<http://rdf.ebi.ac.uk/dataset/reactome/description>

select distinct ?o where {
graph <http://rdf.ebi.ac.uk/dataset/reactome/45> {?s a ?o}
}
select count (distinct ?s) where {
graph <http://rdf.ebi.ac.uk/dataset/reactome/45> {?s ?p ?o}
}
2221044

select count (distinct ?s) where {
?s ?p ?o
}


2221353


owl:Ontology
biopax3:FragmentFeature
biopax3:SmallMoleculeReference
biopax3:TemplateReaction
biopax3:Control
biopax3:EvidenceCodeVocabulary
biopax3:SequenceSite
biopax3:Stoichiometry
biopax3:Catalysis
biopax3:PathwayStep
biopax3:UnificationXref
biopax3:Protein
biopax3:PublicationXref
biopax3:PhysicalEntity
biopax3:CellularLocationVocabulary
biopax3:Complex
biopax3:BiochemicalReaction
...


SPARQL: advanced features
Distributed queries

WHERE
{
SERVICE<http://beta.sparql.uniprot.org/sparql> {?uniprotID <http://purl.uniprot.org/core/
encodedBy> ?o .
?o <http://www.w3.org/2004/02/skos/core#prefLabel> "INS"}
?pathway rdf:type biopax3:Pathway .
?protein biopax3:entityReference ?uniprotID
}
LIMIT 100

Don’t change the order!



SPARQL update
INSERT DATA
{
<http://example/book1> dc:title "A new book" ;
dc:creator "A.N.Other" .
}

INSERT
{ GRAPH <http://example/bookStore2> {
?book ?p ?v }
}
WHERE
{ GRAPH <http://example/bookStore>
{ ?book dc:date ?date .
FILTER ( ?date > "1970-01-01"^^xsd:dateTime )
?book ?p ?v
INSERT DATA { GRAPH <http://example/graph> } }
{
<http://example/book1> dc:title "A new book" ;
dc:creator "A.N.Other" .
}}

DELETE WHERE { ?person foaf:givenName 'Fred';
?property
?value }

http://www.w3.org/TR/sparql11-update/




http://spinrdf.org/



Example of Tools
Reasoners

•

Different varieties: simple transitive reasoning can be
embedded in triplestores

•
•

Rule based
OWL-reasoners (OWL, Fact++): can be stand-alone or
embedded in other applications (e.g.: Protege)

http://www.w3.org/2001/sw/wiki/OWL/Implementations



Example of Tools
RDF visualization tools (graph based)

Think about reiﬁcation! (not always intuitive)
http://semweb.salzburgresearch.at/apps/rdf-gravity/user_doc.html



Example of Tools
OWL visualization tools (class based)

http://protegewiki.stanford.edu/index.php/OWLViz



Example of Tools
Information discovery

http://www.visualdataweb.org/relﬁnder.php



Example of Tools
Integrated environments: mapping, visualization,
editing, analysis, exploration

http://io-informatics.com



Example of Tools
Facet browsers




Example of Tools
Visualization/mashup libraries
<div

id="sgvzl_example1"
data-sgvizler-endpoint="http://sws.ifi.uio.no/sparql/npd"
data-sgvizler-query="SELECT ?class (count(?instance) AS ?noOfInstances)
WHERE{ ?instance a ?class }
GROUP BY ?class
ORDER BY ?class"
data-sgvizler-chart="gPieChart"
style="width:800px; height:400px;"></div>
https://code.google.com/p/sgvizler/




Example of Tools
Infrastructures

http://biordf.net/cardioSHARE/



The Semantic Web Landscape




Primary Resources
Uniprot

RDF available via:
•Content negotiation
•Extension
•Endpoint

Lot of triples!

http://www.uniprot.org
http://www.ebi.ac.uk/rdf/documentation/uniprot/



Primary Resources
EBI RDF Platform




Primary Resources
EBI: indentiﬁers.org




Primary Resources
“Some” literature

Pubmed:
http://togows.dbcls.jp
http://data.nature.com




Primary Resources
Gene Ontology

http://beta.geneontology.org/page/download-ontology


getting OWL!


Primary Resources
Wikipathways (OpenPhacts)

http://sparql.wikipathways.org



Primary Resources
DBPedia

freebase...



Integrated Resources
BioPortal

• Ontologies, mappings and tools
• Most of ontologies are large
terminology collections

http://bioportal.bioontology.org/



BioPortal: detail on SPARQL queries and ontology representation

To be ﬁlled



BioPortal: detail on SPARQL queries and ontology representation




Bio2RDF




http://linkedlifedata.com




DistilBio




PathwayCommons integrates pathway
information via BioPAX

GO: 39k terms



LOD: Linked Open Data

http://lod-cloud.net/versions/2011-09-19/lod-cloud_colored.html



LOD: Linked Open Data




Conclusions
Is the Semantic Web good for you ?

•
•

Are resources reliable ? (a business problem)

•

How can I know which resources to use ?
(Not easy)

•

Is the technology mature ? Part of it.

Does it solve the Semantic Integration problem ?
(No)




Conclusions
Is the Semantic Web good for you ?

•
•
•

Beyond Syntax

•
•

Several interesting resources

Explicit Semantics
Web technologies
(SPARQL as REST, distributed queries)
Some technologies are robust (RDF, SPARQL, ...)




Exercise
•
•
•
•

Get Jena Fuseki
Start the server
Add some triples via the web interface
Make a distributed query to Uniprot

<http://myHomePage> <http://nettab.org/example/chosenProtein> <uniprotID>




Semantic Web for Life Sciences: vision, aims, tools, platforms

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