Introduction to the Semantic Web and
Bio2RDF, the “semantic web atlas of
postgenomic knowledge”
Michael Grobe
Biomedical Applications Group
Research Technologies
University Information Technology Services
Indiana University
1
This presentation in perspective
This is actually one of a series of presentations on Linked Data Web
and graph database technologies:
- Introduction to ontologies
- RDF, Jena, SparQL, and the “Semantic Web”
- This presentation on Bio2RDF
- OWL and inference over ontologies
In general, these Semantic technology topics seem “deceptively
simple,” but are fraught with complications, limitations, and
qualifications…especially when the casual user attempts to
compare them with relational data approaches to the same or similar
problems.
2
Topics
Simple introduction to the semantic approach
- sentences as triples and graphs
- sentences encoded using URIs
- transcending the data/metadata dichotomy with “sentence stores”
Introduction to SparQL
Free-standing query clients: Twinkle, RDF-gravity, Explorator
Bio2RDF atlas (warehouse) contents
Bio2RDF queries using Virtuoso SparQL and iSparQL endpoints
The Bio2RDF proxy relay service, and the tabulator
Discussion of the semantic approach
3
Sentences
Here is some information in sentence form:
Smith has age 21.
Jones has age 45.
Blake has age 12.
George has age 21.
Smith has favorite friend Jones.
Jones has favorite friend Smith.
Blake has favorite friend Blake.
George has favorite friend Smith.
Note that each sentence has the form:
Subject
Predicate
Object
Property
Value
also known as
Entity
4
A “Sentence base”
If someone hadn’t already done it, we could invent a “sentence
base” to hold these sentences, but W3C has already done it.
To help with manipulation and searching, each grammatical
component is stored separately, so that each sentence has a “triple”
form like:
Subject
Smith
Jones
Blake
George
Smith
Jones
Blake
George
Predicate
has age
has age
has age
has age
has favorite friend
has favorite friend
has favorite friend
has favorite friend
Object
21
45
12
21
Jones
Smith
Blake
Smith
5
Sentences
We can query such information with queries like:
“Someone has friend Smith”
where “Someone” acts like a “variable” and “resolves” as the list:
Jones
George
because the pattern “Someone has friend Smith” matches both triples:
Jones
George
has_favorite_friend
has_favorite_friend
Smith
Smith
and we can interpret a more complicated query like:
"Someone has friend Smith and has age 21”
as a pair of requirements:
"Someone has friend Smith” and "Someone has age 21“
where we mean that same someone has both characteristics . . . in which case
Someone will resolve as "George“, since George is the only “Someone” who satisfies
both requirements via the following triples:
George
George
has age
has_favorite_friend
21
Smith
6
Using graphs used to represent sentences
If we want to complicate things, we can also represent the same
information in “graph form” as with these 2 graphs that represent
the 2 kinds of information in the collection of sentences:
Graph #1: Person ages
Graph #2: Favorite Friends
Typically we don’t really want to complicate these issues, but the
semantic web literature often “thinks” in graph terms, so it’s a good
idea to cover the basic idea.
7
Using graphs to represent sentences
Here the 2 graphs are combined using named edges to represent
2 kinds of information associated with the same 4 persons.
Graph #3: Person ages (:age) and favorite friends (:fav)
Each arc represents the “predicate” of a sentence, connecting a
“subject” with an “object”. (Note that a subject may have >= 0 arcs
of each type.)
8
Using URIs and URLs to represent predicates: Metadata!
Now if it hadn’t already happened someone would come up with the idea
to use URLs to point to Web documents that describe the “exact” meaning
of each predicate, or “metadata”.
For example, “http://CelebrityMagazine.com/fav” could contain a definition
of “favorite friend”, and other documents would define “BFF”, “long-timefriend”, “family-friend”, “friends with benefits”, etc,
And, in fact, these definitions could themselves refer to other definitions
like some “superset” of relationships such as:
http://CelebrityMagazine.com/personal_relationships
or the personal_relationships file could include a collection of subset
definitions that we might refer to like:
http://CelebrityMagazine.com/personal_relationships#fav
using the # convention for targeting a specific location within a URL.
Note that this form of metadata is not the only useful form of metadata, but
it is clearly integrated with the data in a unique fashion. (The basic triplet
structure of each sentence represents another (implicit) form of metadata.)
9
The sentences as a set of 8 triples (2 for each person)
|-------------------------------------|
| Subject | Predicate
| Object |
=======================================
| “Blake” | example:fav | “Blake” |
| “Blake” | info:has_age | "12"
|
|
|
“Jones”
“Jones”
| example:fav | “Smith”
| info:has_age | "35"
|
|
|
|
“George” | example:fav | “Smith”
“George” | info:has_age | "21"
|
|
| “Smith” | example:fav | “Jones” |
| “Smith” | info:has_age | "21"
|
---------------------------------------
Here the abbreviation “example:” stands for
http://CelebrityMagazine.com/personal_relationships#
and the abbreviation “info” stands for some imaginary web page that defines age,
let’s say
http://demographicstats.org/characteristics#”.
10
Representing sentence components using URIs
To specify exactly which person named “Blake”, “Smith”, etc. we are referring to, we
can again use URIs.
-----------------------------------------------------------------------------|
Subject
| Predicate
|
Object
|
===============================================================================
| <http://fake.host.edu/blake> | example:fav | <http://fake.host.edu/blake> |
| <http://fake.host.edu/blake> | info:has_age | "12"
|
| <http://fake.host.edu/jones>
| <http://fake.host.edu/jones>
| example:fav | <http://fake.host.edu/smith> |
| info:has_age | "35"
|
| <http://fake.host.edu/george> | example:fav | <http://fake.host.edu/smith> |
| <http://fake.host.edu/george> | info:has_age | "21"
|
| <http://fake.host.edu/smith> | example:fav | <http://fake.host.edu/jones> |
| <http://fake.host.edu/smith> | info:has_age | "21"
|
-------------------------------------------------------------------------------
Here the abbreviation “example:” stands for
http://CelebrityMagazine.com/personal_relationships#
and the abbreviation “info” stands for some imaginary web page that defines age, let’s say
http://demographicstats.org/characteristics#”.
11
Triplestore summary and outrageous claims
Sentences may be represented as a collection of triples.
Sentences in triple form are stored in “triplestores” or “quad stores”
(when many are stored together).
Triples will contain URIs that:
- serve to identify and/or reference predicate definitions,
and object data types, and
- identify and/or name “resources”: subjects and/or
objects.
IMHO, triplestores do NOT contain “data”. They contain “sentences”,
“information”, or “assertions” (not necessarily true or correct
assertions).
One might even say that the semantic approach transcends the
data/meta-data dichotomy because the triple format provides
implicit metadata, and because predicates link to metadata and/or
the option to link to metadata in every triple, and because subjects
and objects often link to external resources.
12
Triples may be serialized in various forms:
- using the N3 version of Turtle to create files that look like the
previous example with each line holding 3 URIs (and ending with
a “.”)
- using the Resource Description Format (RDF), as in this encoding
of the Smith information (with non-dereferenceable URIs):
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:example="http://fake.host.edu/example-schema#">
<example:Person
rdf:about=“http://fake.host.edu/smith”>
<example:name>Smith</example:name>
<example:age>21</example:has_age>
<example:fav
rdf:resource=“http://fake.host.edu/jones”/>
</example:Person>
</rdf:RDF>
13
Dereferenceable URI version of the Smith RDF triple
- using the Resource Description Format (RDF), as in this encoding
of the Smith information including “dereferenceable” URIs:
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:example="http://fake.host.edu/example-schema#">
<example:Person
rdf:about=“http://discern.uits.iu.edu:8421/smith”>
<example:name>Smith</example:name>
<example:age>21</example:has_age>
<example:fav
rdf:resource=“http://discern.uits.iu.edu:8421/jones”/>
</example:Person>
</rdf:RDF>
14
Browse RDF documents
Here is a view of the Smith RDF file from within Firefox using the Tabulator plug-in:
You can click on the jones.rdf link to see the Jones record, and browse from there, or
choose the Person link to examine its definition (if its dereferenceable).
15
The “Semantic Web”
In general, if URIs are dereferenceable they can
link into a “Gigantic Global Graph”, usually
know as the Linked Data Web or the “Semantic
Web,” with RDF as one of W3C’s Semantic Web
architectural levels.
“If HTML and the Web make all online documents
look like one huge book, RDF, schema, and
inference languages will make all the data in the
world look like on huge database.”
--TimBL
16
Documents in RDF and N3 format may be interrogated:
- by physical inspection (for anyone willing to read XML)
- by writing programs (in Jena, for example) that read RDF files,
construct the represented graphs internally, and then
- access graph triples in sequential order,
- select triples according to specified content, and/or
- apply SparQL queries and access results in sequential order
- using command-line tools that apply SparQL queries, and/or
- using GUI interfaces accepting SparQL queries
- written in text, or
- represented graphically
17
A SparQL example
If
http://discern.uits.iu.edu:8421/all-persons.rdf
contains all the triples listed earlier, then this SparQL query should find all the
triples related to “smith”:
select
$p $o
from <http://fake.host.edu:8421/all-persons.rdf>
where
{
<http://discern.uits.iu.edu:8421/smith.rdf> $p $o .
}
Intuitively, this query asks “Smith has what relationship(s) to whom/what?”
and should identify these 2 value pairs:
<http://fake.host.edu/example-schema#fav>
<http://discern.uits.iu.edu:8421/jones.rdf>
<http://fake.host.edu/example-schema#age> "21”
$p, $o are variable names that were each assigned a value as the query was
“satisified.” Variable names may also start with “?”.
18
Another SparQL example
If
http://discern.uits.iu.edu:8421/all-persons.rdf
contains all the triples listed earlier, then this SparQL query simply asks for a
list of all those triple values:
select
*
from <http://discern.uits.iu.ed:8421/all-persons.rdf>
where
{
$sub $pred $obj .
}
Intutitively, this query asks “Who has what relationship to whom?”
$sub, $pred, and $obj will each be assigned one or more values as the
query is satisified and all three will be printed (*).
(Note that “$sub $pred $obj .” is a triple pattern in the Turtle/N3
format.)
19
Results of the single file SparQL query
-------------------------------------------------------------------------| sub
| pred
| obj
|
==========================================================================
| http://...8421/blake.rdf | example:fav
| http://...8421/blake.rdf |
| http://...8421/blake.rdf | example:has_age | "12"
|
| http://...8421/jones.rdf
| http://...8421/jones.rdf
| example:fav
| http://...8421/smith.rdf |
| example:has_age | "35"
|
| http://...8421/george.rdf | example:fav
| http://...8421/smith.rdf |
| http://...8421/george.rdf | example:has_age | "21"
|
| http://...8421/smith.rdf | example:fav
| http://...8421/jones.rdf |
| http://...8421/smith.rdf | example:has_age | "21"
|
--------------------------------------------------------------------------
where “…” indicates “discern.uits.iu.edu:”.
20
A “distributed” SparQL query against 4 separate RDF
files
The next query searches 4 dereferenceable files holding
the same data broken into 4 files, one for each subject:
select *
from <http://discern.uits.iu.edu:8421/smith.rdf>
from <http://discern.uits.iu.edu:8421/jones.rdf>
from <http://discern.uits.iu.edu:8421/george.rdf>
from <http://discern.uits.iu.edu:8421/blake.rdf>
where
{
$sub $pred $obj .
}
The results of this query will be the same as the results
for the single file query (though order my vary due to
remote URL access latency).
21
Use SparQL to find the predicates
This SparQL example query simply asks for a list of all
the unique predicates that occur in all the triples:
select
distinct $p
from <http://discern...8421/friend-network.rdf>
where
{
$s $p $o .
}
If you don’t use “distinct” you will get multiple occurrences
of the same predicate.
This can be very useful when you are trying to figure out
what predicates are available to interrogate a triplestore
that you don’t know much about.
22
SparQL (incomplete) basic syntax
:
SELECT
some_variable_list
FROM
<some_RDF_source_URI>
WHERE
{
{ some_n3_triple_pattern .
another n3_triple_pattern .
}
Notes:
- the “<“ and “>” characters are required; and “[“ and “]” surround optional content.
- other commands in place of SELECT are: CONSTRUCT, ASK and DESCRIBE,
- * is a valid variable list, specifying any variable returned by the query engine, and
may be preceded by DISTINCT, which will prevent duplicate triples
- there may be multiple FROM clauses, whose targets will be combined and treated as
a single store,
- a “.” separating multiple triple patterns is intuitively similar to an “and” operator (but
actually behaves like an SQL natural join,
- the term WHERE is optional, and may be omitted.
SparQL reference: http://www.dajobe.org/2005/04-sparql/SPARQLreference-1.8.pdf
23
Optional clauses in SparQL queries
Permitted within “where” clauses:
optional { triple_pattern }: identifies a triple that need not appear in an RDF target but
whose absence will not prohibit a pattern match.
filter: restricts variable matches in the preceding triple to specified filter patterns, as in:
{ $s $p $date FILTER ( $date > "2005-01-01T00:00:00Z"^^xsd:dateTime ) }
or
{ $s $p $d FILTER ( xsd:dateTime( $d ) < xsd:dateTime( "2005-01-01T00:00:00Z“ ) ) }
or
{ ?s ?p ?name FILTER regex( ?name, "^smi", “some_flag“ ) }
union: “where” clauses may be constructed as
{ triple_pattern_1 } UNION { triple_pattern_2 }
and any RDF element matching either of these triples will be included in the resulting
output.
Permitted following the “where” clause:
order by [DESC|ASC| ] ( variable_list )
limit n: print up to n return values.
offset n: start output with the nth return value.
24
Some useful SparQL pattern patterns
Display two property values of some entity (<some_URI>) on the same line:
select *
where
{
<some_URI> <some_predicate> ?o .
<the_same_URI> <some_other_predicate>
}
?o1.
Example using the friend information and PREFIX statements:
PREFIX example:
<http://CelebrityMagazine.com/personal_relationships#>
PREFIX info: <http://demographicstats.org/characteristics#>
select *
where
{
<http://fake.host.edu/smith>
<http://fake.host.edu/smith>
example:fav
info:has_age
?favorite .
?age .
}
25
Some useful SparQL pattern patterns
Merge results of 2 pattern matches into a single output column:
select *
where
{
{ <some_URI> <some_predicate> ?o . }
UNION
{ <some_other_URI> <some_other_predicate> ?o . }
}
Example:
PREFIX example:
<http://CelebrityMagazine.com/personal_relationships#>
PREFIX info: <http://demographicstats.org/characteristics#>
select *
where
{
{ <http://fake.host.edu/smith>
UNION
{ <http://fake.host.edu/smith>
}
example:fav
info:has_age
?values .}
?values . }
26
Some useful SparQL pattern patterns
Slowly find all triples whose object components mentions hexokinase:
select *
where
{
?s ?p ?o .
}
FILTER regex( $o, "hexokinase" ) .
Quickly find all entries with object components mentioning hexokinase,
but works only within a Virtuoso triplestore when applied to indexed
graphs (and will return nothing when applied to a non-indexed graph):
select *
where
{
?s1 ?p1 ?o1 .
?o1 bif:contains "hexokinase" .
}
27
SparQL desktop client: Twinkle (version of the upward paths query)
28
SparQL desktop client: RDF-gravity (the friend data)
29
SparQL desktop client: Explorator RDF explorer
The Explorator can download (extracts from) multiple RDF resources, and
manipulate them in combination. Here with the Russian lakes example.
This approach provides an interface using a set algebra model of data
manipulation. (See Araujo, et al. and http://139.82.71.60:3000/explorator)
30
SparQL endpoints
Triplestores like the Virtuoso Universal Database System and the D2R
gateway will take SparQL queries through several interfaces:
- encoded in URLs addressed to the triplestore servers, like
http://dbpedia.org/sparql?query=SELECT distinct *
WHERE { $s $p $o .
$o bif:contains “Goethe_Johann_Wolfgang” . }
- entered into Web forms that present text areas into which one can
enter queries, as on the next page
31
SparQL endpoints
32
Using SparQL endpoints to get RDF documents
Documents returned by SparQL queries are not RDF documents. They may not have
triples and they are structured for display or storage in HTML, Excel or some other format.
However, you can use the CONSTRUCT command (in place of SELECT) within a SparQL
query to build an RDF formatted response.
construct
{ ?o <http://www.w3.org/2000/01/rdf-schema#comment> ?q }
where
{
<http://bio2rdf.org/go:0004003 <http://bio2rdf.org/ns/go#is_a> ?o .
?o
<http://www.w3.org/2000/01/rdf-schema#comment>
?q .
}
The structure of the triple to be created is defined in the “construct” clause, and the
returned document is shown on the next page.
You can also send a CONSTRUCT query to a SparQL endpoint embedded within a URL,
as in (here shown without the required URL encodings):
http://discern.uits.iu.edu:8890/sparql?query=construct
{ ?o <http://www.w3.org/2000/01/rdf-schema#comment> ?q }
where
{ <http://bio2rdf.org/go:0004003> <http://bio2rdf.org/ns/go#is_a> ?o .
?o <http://www.w3.org/2000/01/rdf-schema#comment> ?q . }
Do this while using clients like the Explorator to get extracts from very large triplestores
for local manipulation. Otherwise such triplestores may not be locally manageable.
33
Using SparQL endpoints to get RDF documents
Here’s what the previous CONSTRUCT query will return (edited for readability). These
are the parents of go:0004003:
<?xml version="1.0" encoding="utf-8" ?>
<rdf:RDF xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns#
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#">
<rdf:Description rdf:about="http://bio2rdf.org/go:0008094">
<rdfs:comment>Catalysis of the reaction: ATP + H2O = ADP + phosphate
in the presence of single- or double-stranded DNA; drives another
reaction.
</rdfs:comment>
</rdf:Description>
<rdf:Description rdf:about="http://bio2rdf.org/go:0003678">
<rdfs:comment>Catalysis of the reaction: NTP + H2O = NDP + phosphate
to drive the unwinding of a DNA helix.
</rdfs:comment>
</rdf:Description>
<rdf:Description rdf:about="http://bio2rdf.org/go:0008026">
<rdfs:comment>Catalysis of the reaction: ATP + H2O = ADP + phosphate
to drive the unwinding of a DNA or RNA helix.
</rdfs:comment>
</rdf:Description>
</rdf:RDF>
34
Bio2RDF: Atlas of postgenomic knowledge
Bio2RDF integrates some 40 biomedical information resources
(such as GO, Uniprot, etc.) or extracts recoded in RDF:
- currently runs over the Virtuoso Universal Database server at
http://atlas.bio2rdf.org
but each resource has its own SparQL endpoint, in addition to
the endpoint accessing the unified triplestore:
http://atlas.bio2rdf.org/sparql
- a list of included resources is at
(http://www.freebase.com/view/user/bio2rdf/public/sparql)
and includes links to the SparQL endpoint for each resource,
as well as descriptions of the resource contents and triple counts.
- there is also a Bio2RDF proxy service that takes queries and
relays them to multiple distributed servers (examples later).
35
Resources included in Bio2RDF
(downloadable from http://quebec.bio2rdf.org/download/n3/)
GO
OMIM
PUbMed
GeneID
UniProt
UniRef
UniParc
Kegg Pathway
CPATH
Reactome
Biocyc
MeSH
PDB
CPD: Kegg Ligand for chemical compound
GL: Kegg Ligand for carbohydrate structure
EC
RN Kegg Ligand for chemical reaction
DR: Kegg Ligand for drugs
Taxonomy: NEWT
PID
KEGG
HGNC
INOH
IProClass
MGI
CellMap
BioPAX
InterPro
Pfam
PROSITE
Protein
SID
CID
PubChem
UniSTS
Homologene
DBpedia
OBO
CheBI
Affymetrix
Biocarta
36
Bio2RDF resources
(Edge width is proportional to link density.)
37
Local Bio2RDF (partial) mirror
Research Technologies has installed a TEST version of a Virtusoso
server hosting a PART of Bio2RDF (bind, GO, and IPROCLASS only)
running locally on discern.uits.iu.edu.
You can reach its SparQL endpoint at
http://discern.uits.iu.edu:8890/sparql
(Firefox and IE)
The isparql endpoint is only usable via Firefox, and is accessible at
http://discern.uits.iu.edu:8890/isparql (Firefox only)
(Choose "Cancel" in the Preferences pop-up to use it.)
Note that these endpoints are only available in TEST mode; they could go
away at any time.
38
Find parents of GO:0004003 in the local Bio2RDF GO
graph using the SparQL endpoint
select
*
where
{
<http://bio2rdf.org/go:0004003>
<http://bio2rdf.org/ns/go#is_a>
$parent .
}
Result:
----------------------------------| parent
|
===================================
| <http://bio2rdf.org/go:0008094> |
| <http://bio2rdf.org/go:0008026> |
| <http://bio2rdf.org/go:0003678> |
----------------------------------39
Find all 3-element paths up from GO:0004003
PREFIX go: <http://bio2rdf.org/ns/go#>
select
*
where
{
<http://bio2rdf.org/go:0004003>
go:is_a
$a .
$a
go:is_a
$b .
$b
go:is_a
$c .
}
Note the use of the PREFIX to define an abbreviation that will be
substituted for the string “go:”.
Also, you can speed up this search by specifying
http://bio2rdf.org/go
as the “Default Graph URI” (so the other graphs will be ignored).
40
Find all 3-element paths up from GO:0004003 using
Bio2RDF
a
b
c
http://bio2rdf.org/go:0008026
http://bio2rdf.org/go:0070035
http://bio2rdf.org/go:0004386
http://bio2rdf.org/go:0008026
http://bio2rdf.org/go:0042623
http://bio2rdf.org/go:0016887
http://bio2rdf.org/go:0003678
http://bio2rdf.org/go:0004386
http://bio2rdf.org/go:0017111
http://bio2rdf.org/go:0008094
http://bio2rdf.org/go:0042623
http://bio2rdf.org/go:0016887
41
Find all 3-element paths up from GO:0004003 using SQL within CLSD
select
a.parent_id, b.parent_id, c.parent_id
from
GO.molecular_function_DAG a
join
GO.molecular_function_DAG b
on
a.parent_id = b.child_id
join
GO.molecular_function_DAG c
on
b.parent_id = c.child_id
where
a.child_id like 'GO:0004003‘
This query is posed as a series of joins on the GO.molecular_function_DAG
just as the SparQL version uses structures like:
$a go:is_a
$b go:is_a
$b .
$c .
where go:is_a is analogous to the DAG table, the “.” specifies a “join”, and $b,
appearing on two separate lines, implicitly specifies an equality requirement.
42
Auer and Lehmann asked:
“What DO Innsbruck and Leipzig have in common?”
. . .or to be more exact:
What query will reveal what properties 2 entities have in common?
select *
where
{
< . . . Innsbruck>
< . . . Leipzig>
}
?p
?p
?o .
?o .
will direct the resolver will find every characteristic of each city and
see which pairs of cities share the same characteristic.
This doesn't have an equivalent in SQL because you can't treat table
and variable names as variables in SQL.
(You can of course get around this by storing all your data denormalized as a single table containing 3 columns, which might not be
a bad idea in some circumstances.)
43
What do go:0004145 and go:0004059 have in common?
select *
where
{
<http://bio2rdf.org/go:0004145> $predicate ?object .
<http://bio2rdf.org/go:0004059> $predicate ?object .
}
---------------------------------------------------------------|
predicate
|
|
object
|
|--------------------------------------------------------------|
| http://bio2rdf.org/ns/go#is_a
|
|
http://bio2rdf.org/go:0008080
|
---------------------------------------------------------------|
| http://www.w3.org/1999/02/22-rdf-syntax-ns#type
|
|
http://bio2rdf.org/ns/go#Term
|
|--------------------------------------------------------------|
| http://www.w3.org/1999/02/22-rdf-syntax-ns#type
|
|
http://bio2rdf.org/ns/go#molecular_function |
---------------------------------------------------------------So, this query reveals that both classes are subclasses of go:0008080.
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Some queries for Bio2RDF (atlas.bio2rdf.org/sparql)
Find every triple whose subject is http://bio2rdf.org/iproclass:P04637: (or is it P31946?)
select * where
{
<http://bio2rdf.org/iproclass:P04637> ?p1 ?o1 .
}
Find all the subjects that cross reference TO http://bio2rdf.org/geneid:3098:
select * where
{
?s ?p <http://bio2rdf.org/geneid:3098> .
}
Get all the pubmed predicates:
select * where
{
<http://bio2rdf.org/pubmed:10978502> $p $o .
}
Get all the Pubmed titles and abstracts about geneid 3098:
select distinct $title $abstract where
{
<http://bio2rdf.org/geneid:3098> <http://bio2rdf.org/ns/bio2rdf#xArticle>
?o .
$o
<http://purl.org/dc/elements/1.1/title>
$title .
$o
<http://www.w3.org/2000/01/rdf-schema#comment>
$abstract .
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}
Some queries for discern.uits.iu.edu:8890/sparql
GO categories and descriptions for TP53 aka P04637:
select * where
{
<http://bio2rdf.org/iproclass:P04637>
<http://bio2rdf.org/ns/iproclass#xGo>
$go .
$go <http://www.w3.org/2000/01/rdf-schema#comment>
$description .
}
Same but for molecular function namespace only and using PREFIXes:
PREFIX iproclass: <http://bio2rdf.org/iproclass:>
PREFIX iproclass-ns:
<http://bio2rdf.org/ns/iproclass#>
PREFIX rdf-schema: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX rdf-syntax: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX go-ns:
<http://bio2rdf.org/ns/go#>
select * where
{
iproclass:P04637 iproclass-ns:xGo
$go_cat .
$go_cat
rdf-schema:comment $go_description .
$go_cat
rdf-syntax:type
go-ns:molecular_function
}
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Query dbpedia for entries about “Goethe”
using the Virtuoso iSparql text interface
Note that the predicate “bif:contains” is a Virtuoso “Built-In Function”
that searches back-end text indexes. It might be possible to search
using a standard SparQL regex FILTER, but it would be much slower.
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The same query using the iSparql “graphical” QBE (sic) interface
Here is the same query in graphical form as constructed using the
iSparql QBE interface:
Components can be dragged-and-dropped from the menu at the top of
the window. The whole interactive window is shown on the next page.
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The same query within the whole iSparql QBE (sic) window
49
Results from the iSparql text and/or QBE queries
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Bio2RDF proxy service
The proxy service is
- a Java servlet that will relays queries to federated versions of
Bio2RDF resources.
- one instance is currently available at
http://atlas.bio2rdf.org/
It will let you run various demo queries, which are much more tractable
if you have the Tabulator plug-in installed.
The "Demonstration set of Bio2RDF URIs" is a particularly interesting
browse.
The next 2 slides show results from the GO demo example.
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Bio2RDF proxy results for GO 0032283
If you select the GO query example and are not running the Tabulator, you will get a
document to download whose contents look like this:
<?xml version="1.0" encoding="UTF-8" ?>
<!-- bio2rdf sourceforge package version (0.6.1) -->
<!-- bio2rdf sourceforge subversion copy Id ($Id: atlas2rdf.jsp 592 2009-06-29 03:09:31Z p_ansell $) -->
<!-- bio2rdf sourceforge properties file subversion copy Id ($Id: bio2rdf.properties 590 2009-06-29 01:38:38Z p_ansell $)
-->
<!-- Query successful on endpoint=http://obo.bio2rdf.org/sparql query=CONSTRUCT {
&lt;http://bio2rdf.org/go:0032283&gt; ?p ?o . } WHERE { &lt;http://bio2rdf.org/go:0032283&gt; ?p ?o . } LIMIT
2000 OFFSET 0-->
<rdf:RDF
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:n0pred="http://bio2rdf.org/ns/go:"
xmlns:ns0pred="http://www.w3.org/2002/07/owl#">
<rdf:Description rdf:about="http://bio2rdf.org/go:0032283">
<rdf:type rdf:resource="http://bio2rdf.org/ns/go:Term"/>
<n0pred:accession>GO:0032283</n0pred:accession>
<rdfs:label>plastid acetate CoA-transferase complex [go:0032283]</rdfs:label>
<n0pred:definition>An acetate CoA-transferase complex located in the stroma of a plastid.</n0pred:definition>
<rdf:type rdf:resource="http://bio2rdf.org/ns/go:term"/>
<n0pred:name>plastid acetate CoA-transferase complex</n0pred:name>
<n0pred:is_a rdf:resource="http://bio2rdf.org/go:0009329"/>
</rdf:Description>
</rdf:RDF>
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Bio2RDF proxy results for GO:0032283
If you select the GO example and are running the Tabulator in Firefox, you can
end up with a browsable page with “tabulated and N3 results like:
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Evaluate the semantic approach?
The semantic approach is complicated, often produces ugly-looking
and slow results, and new tools emerge like Topsy . . .
. . . but it does some things really well, things that cannot be so easily
done within the relational approach:
- It handles some kinds of distributed information well; users can
access multiple RDF documents in a single SparQL query, and even
browse distributed RDF sources as part of the LDW or GGG.
- It simplifies the integration of (parts of) resources; since it doesn’t
require establishing a unified storage schema, multiple RDF versions
of multiple resources can be dumped into the same triplestore.
- It merges data with metadata in a unique fashion, making metadata
easy to find.
- Since it stores information based on sentences, it’s easy for users to
understand the storage format and make extracts.
- Its sentence based query language, SparQL, is more intuitive than
SQL (and is more declarative than SQL?).
- It can handle some types of queries much more easily than SQL
(Leipzig and Innsbruck).
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For more information, see:
• Auer, Soren and Jens Lehmann, "What do Innsbruck and Leipzig
have in common? Extracting Semantics from Wiki Content,
European Semantic Web Conference (ESWC), 2007.
• Bizer, Christian, Tom Heath, Tim Berners-Lee, “Linked Data--The
story so far.”
http://tomheath.com/papers/bizer-heath-berners-lee-ijswis-linkeddata.pdf
• Grobe, Michael, “RDF, Jena, SparQL, and the “Semantic Web”,
SIGUCCS, 2009.
http://mypage.iu.edu/~dgrobe/SIGUCCS/fp0518-grobe.pdf
• Marajo S.; Schwabe D., Barbosa S. - Experimenting with Explorator:
a Direct Manipulation Generic RDF Browser and Querying Tool.
Visual Interfaces to the Social and the Semantic Web (VISSW 2009),
Sanibel Island, Florida - February 2009
http://smart-ui.org/events/vissw2009/papers/VISSW2009-Araujo.pdf
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