Transcript people.ku.edu

Introduction to RDF, Jena, SparQL, and the
“Semantic Web”
Michael Grobe
Pervasive Technology Institute
Indiana University
October 12, 2009
This presentation in perspective
This is actually one of a series of presentations on
Linked Data Web and semantic technologies:
- Introduction to ontologies
- This on RDF, Jena, SparQL, and the “Semantic Web”
- Using inference and OWL
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
- sentence components encoded using URIs
- serializing sentences using the Resource Description Format (RDF)
- storing semantically encoded data in triplestores
- browsing information encoded in RDF
Accessing and querying semantic data
- Introduction to SparQL
- Free-standing query clients: Twinkle, RDF-gravity, Explorator
- Jena: software for manipulating triples
Preeminent semantic resources
- DBpedia
- Bio2RDF “semantic web atlas of postgenomic knowledge”
- Queries using Virtuoso SparQL and iSparQL endpoints
Ontologies: what are they and how are they used?
Discussion of the semantic approach
3
From raw data to sentences
Here is some information that might be useful to you:
Smith
Smith
21
Jones
Do you get it?
Would it help to see the data tables? Perhaps you could guess what
I’m trying to say if you look at column names.
What’s missing here: the “relationships” between the separate
pieces of “data”.
In natural languages these relationships are established by using
“predicates” to form sentences that connect these components,
. . . as in the sentences on the next slide:
4
Sentences
. . . 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.
where each sentence has the form:
Subject
Predicate
Object
Property
Value
also known as
Entity
and these elements are known together as a “triple”.
5
A “Sentence base”
We can put these triples into one or more files to build a “sentence
base” to hold these sentences.
To help with manipulation and searching, each grammatical
component is stored and accessed separately, so that each
sentence retains its triple form:
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
6
Query 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
7
Query sentences
We can interpret a more complicated query like:
"Someone has favorite friend Smith and has age 21?”
as a pair of requirements:
"Someone has favorite 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
Note that in both example we have used triple “patterns” to query
the triple store
8
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 and some
applications display results as visual graphs.
9
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.)
10
Using URIs and URLs to identify predicates and 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 provides another (implicit) form
of metadata.
11
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#”.
12
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#”.
13
Triplestore summary and outrageous claims
Sentences are composed of subject, predicate, object “triples”.
Subjects and predicates are specified as URIs that may be dereferenceable,
and predicate URLs may provide metadata describing the meaning of the
predicate.
A collection of triples can be represented as a “graph”, and may be known as
a “graph.”
Sentences are stored in “triplestores” or “quad stores” (when they are
members of identifiable graphs whose names give the 4th component).
Triples will contain URIs that:
- identify and/or name “resources”: subjects and/or objects, and
- serve to identify and/or reference predicate definitions,
and object data types (as in “25”^^xsd:int), and
One way to think about this, is that triplestores do NOT contain “data”, but
rather “sentences”, “information”, “assertions” (not necessarily true or correct
assertions), “units of thought” (Mons), or maybe “little chunks o’ meaning”.
One might also say that the semantic approach transcends the data/metadata dichotomy because the triple format provides implicit metadata, and
because predicates can link to their definitions.
14
Triples may be serialized in various forms
There are several ways to convert such triples into a serialized, or text-based,
form. Here is the simplest. It is the N3 (for Notation 3) form of a standard known
as “Turtle” (for “Terse RDF Triple Language”), with each line holding 3 URIs, and
ending with a “.”
@prefix example <http://CelebrityMagazine.com/personal_relationships#>
@prefix info <http://demographicstats.org/characteristics#>
.
.
<http://fake.host.edu/blake>
<http://fake.host.edu/blake>
example:fav
info:has_age
<http://fake.host.edu/blake>
"12"
.
.
<http://fake.host.edu/jones>
<http://fake.host.edu/jones>
example:fav
info:has_age
<http://fake.host.edu/smith>
"35"
.
.
<http://fake.host.edu/george>
<http://fake.host.edu/george>
example:fav
info:has_age
<http://fake.host.edu/smith>
"21"
.
.
<http://fake.host.edu/smith>
<http://fake.host.edu/smith>
example:fav
info:has_age
<http://fake.host.edu/jones>
"21"
.
.
15
Triples may be serialized in various forms:
Another serialization format is the standard Resource Description Format
(RDF), which is used in this encoding of the Smith information (with nondereferenceable 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>
Note: There exist other, “standard” schemas for encoding personal information,
such as the Friend of a Friend (FOAF) schema.
16
Dereferenceable URI version of the Smith RDF triple
Here is the same information encoded with “dereferenceable” URIs,
URIs that can actually be accessed and from which content can be
downloaded:
<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>
17
Browsing 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).
18
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.”
“If HTML and the Web make all online
documents look like one huge book, RDF,
schema, and inference languages will
make all the data (sic) in the world look like
on huge database.”
--TimBL
19
Documents in RDF format may be interrogated:
- by physical inspection (for anyone willing to read XML)
- by using an RDF browser (like the Tabulator plug-in, etc.)
- 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
- using SparQL endpoints that accept queries embedded in URLs
20
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 “?”.
21
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.)
22
Results of the single (unified) 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:”.
23
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).
24
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.
25
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.
- other commands in place of SELECT are: CONSTRUCT, ASK and DESCRIBE.
- * is a valid variable list, specifying any variable included in a triple pattern, 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 a natural language
“and”, 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
26
Optional clauses in SparQL queries
Clauses permitted within a “where” clause:
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.
Clauses 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.
group by: implemented by some SparQL implementations.
27
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 .
}
28
Some more 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 . }
29
Some more useful SparQL pattern patterns
Slowly find all triples whose object components mention “hexokinase”:
select *
where
{
?s ?p ?o .
}
FILTER regex( $o, "hexokinase" ) .
Quickly find all entries with object components mentioning hexokinase,
but works only through a Virtuoso SparQL endpoint 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" .
}
30
SparQL desktop client: Twinkle (version of the upward paths query)
31
SparQL desktop client: RDF-gravity (using the friend data)
32
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)
33
Jena
The Java-based Jena package from HP Labs allows users to manipulate and
query RDF graphs. You can write a program that uses Jena classes to
- retrieve and parse an RDF file containing a graph or a collection of graphs,
- store it in memory,
- examine each triple in turn, examine one component (say, the subject) of
each triple in turn, or examine only triples that meet specified criteria, and,
- write a serialized version of a graph to a file or STDOT.
For example, one might examine each stored triple searching for a specific
reference URI, or for a specific literal value, as with a search for triples
containing a specific value, “21”^^xsd:age, in their object portions.
An RDF graph is stored in Jena as a “model”, and a Jena model is created by a
factory, as in:
Model m = ModelFactory.createDefaultModel();
Once a model has been defined, Jena can populate it by reading data from
files, backend data bases, etc. in various formats, and once it has been
populated, Jena can perform set operations on pairs of populated models
and/or search models for specific values or combinations (patterns) of values.
34
Jena
For example, there are several methods for creating iterators over a model to
access specific components. Iterators may be built by
- listing the components of each triple:
- model.listSubjects();
- model.listObjects();
- comparing a specific component with a specified value, as in:
model.listSubjectsWithProperty( Prop p, RDFNode object );
which will get you a collection of subjects possessing property/predicate
p and specific value object )
- comparing all components against specific values in 2 steps:
- construct a “selector” possessing specific values s, p and o:
Selector selector = new SimpleSelector(
subject, predicate, object )
- and then build the statement list:
model.listStatements( selector );
35
Preeminent Linked Data resources:
The DBpedia and Bio2RDF
The “DBpedia is a community effort to extract structured information
from Wikipedia and to make this information available on the Web”
(http://dbpedia.org/About)
DBpedia currently holds over 200 million triples, harvested by scraping
DBpedia Infoboxes included within the Wikipedia.
The DBpedia is currently housed in a OpenLink Virtuoso Universal
Database, which can store relational, object, XML, and semantic
information.
Details at: http://dbpedia.org/About
36
Bio2RDF: “Atlas of postgenomic knowledge”
Bio2RDF integrates (extracts from) some 40 biomedical information
resources (such as GO, Uniprot, etc.) recoded in RDF (>2 Gtriples):
- 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.
- raw text N3 formats for this data use around 1 TB, but install in
much less space within Virtuoso (perhaps 100 GB).
- there is also a Bio2RDF proxy service that takes queries and
relays them to multiple distributed servers (examples later).
37
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
38
Bio2RDF resources
(Edge width is proportional to link density.)
39
SparQL endpoints
Triplestores like the Virtuoso Universal Database Server publish
“SparQL endpoints” that will take SparQL queries through several
interfaces.
For example you can query the DBpedia through a Virtuoso SparQL
endpoint at
http://dbpedia.org/sparql
by sending SparQL queries:
- encoded in URLs addressed to the triplestore endpoint, 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 pages
40
The SparQL interface to DBpedia
41
The iSparQL Advanced interface to DBpedia
42
The iSparQL QBE interface to DBpedia (close up)
Here is the same query in graphical form as constructed using the iSparql QBE
interface:
43
The iSparQL QBE interface to DBpedia
44
Results from the iSparql text and/or QBE queries
45
Using SparQL to get RDF extracts
Suppose you want to build a local RDF triplestore from DBpedia containing only
the Goethe entries, or import these entries into some other desktop client like
the Explorator.
Documents returned by SparQL select queries are usually not RDF documents.
They may not have triples, and they are usually structured for display or storage
in HTML, Excel or some other format.
You can use the CONSTRUCT command (in place of SELECT) within a SparQL
query to build a proper RDF formatted response:
construct
{
<http://dbpedia.org/resource/Johann_Wolfgang_von_Goethe> $p $o
}
where
{
<http://dbpedia.org/resource/Johann_Wolfgang_von_Goethe> $p $o .
}
The structure of the triple to be created is specified in the “construct” clause.
Note that construct queries like these can be embedded in URLs to SparQL
endpoints.
46
Ontologies
The term “ontology” is used in different ways by different
people.
Pidcock writes that “People use the word to mean different
things, e.g.: glossaries and data dictionaries, thesauri and
taxonomies, schema and data models, and formal
ontologies and inference.”
And Uschold writes “An ontology may take a variety of
forms, but necessarily it will include a vocabulary of terms,
and some specification of their meaning. . .This includes
definitions and an indication of how concepts are interrelated which collectively impose a structure on the domain
and constrain the possible interpretations of terms.”
47
The DBpedia ontology
The DBpedia ontology is “shallow, cross-domain” ontology. In
http://www4.wiwiss.fu-berlin.de/dbpedia/dev/ontology.html
it appears as a tree with maximum depth of 4.
The main level class is a “Thing”, and the first sublevel classes are:
Person, Organization, Anatomical structure, Place, Species, etc.
The next level persons are Scientist, College Coach, Monarch,
Politician, etc.
Some classes are also assigned “properties”. For example, a species
may have Order and Family properties (even though an organism’s
Order and Family could be inferred from its position in the (ontology
that is the) evolutionary tree.
48
Wikipedia Infoboxes
The DBpedia gets its information from
the Wikipedia “Infoboxes”, such as this
one for Johann Wolfgang von Goethe
that appears on his Wikipedia page.
Infobox contents are mapped to
DBpedia ontology classes and
properties, which are used as RDF
predicates.
Here the Goethe “resource” is:
http://dbpedia.org/resource/
Johann_Wolfgang_von_Goethe
and you know how to find all the
predicates and objects by now?
49
The DBpedia ontology
Here is a query to find all the “Places” known to DBpedia:
select distinct *
where
{
$s a <http://dbpedia.org/ontology/Place>
} limit 1000
And a query to find every “person’s” birth info:
select $s $o
where
{
$s a <http://dbpedia.org/ontology/Person> .
$s <http://dbpedia.org/property/birth> $o
} limit 1000
where the predicate “a” is a short form of
http://www.w3.org/1999/02/22-rdf-syntax-ns#type
50
The DBpedia “faceted browser”
DBpedia ontology and property components are displayed in the left
column and can be used is used to define filters for viewing content.
51
GO: An example biomedical ontology (or 3)
Consider the Gene Ontology, very widely used in, and
probably crucial for, bioinformatics and biological research.
The Gene Ontology actually has 3 major components, or
separate sections for defining terms related to
- Biological Process,
- Cellular Component (physical structures or
locations within biological cells), and
- Molecular Function,
each of which defines several thousand terms.
52
A small portion of the Molecular Function portion of the
Gene Ontology Directed Acyclic Graph (DAG)
53
Find parents of GO:0004003 in the example GO DAG
using a SparQL query
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> |
----------------------------------54
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:”.
55
Find all 3-element paths up from GO:0004003 using
the example GO DAG
a
b
c
http://bio2rdf.org/go:0008026
http://bio2rdf.org/go:0004386
http://bio2rdf.org/go:0008047
http://bio2rdf.org/go:0008026
http://bio2rdf.org/go:0016887
http://bio2rdf.org/go:0008047
http://bio2rdf.org/go:0003678
http://bio2rdf.org/go:0004386
http://bio2rdf.org/go:0008047
http://bio2rdf.org/go:0008094
http://bio2rdf.org/go:0016887
http://bio2rdf.org/go:0008047
56
Find all 3-element paths up from GO:0004003 using SQL
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
$b
go:is_a
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.
57
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 characteristic is shared by both cities.
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 using system tables, or by storing
all your data “normalized” as a single table containing 3 columns, which
might not be a bad idea in some unusual circumstances.)
58
Auer and Lehmann asked:
“What DO Innsbruck and Leipzig have in common?”
. . .or to extend this train of thought:
What query will reveal what properties Innsbruck and Leipzig do NOT have in
common?
And can these ideas be extended to notions of “semantic similarity” or “semantic
distance” between resources.
Or extended to a notion of “semantic clustering”?
We might want to ask questions like:
Which cities are most like Innsbruck?
Which cities are most unlike Innsbruck?
Which cities are more like Innsbruck than any other city?
How can we cluster cities into functional groups?
If we can sell gadgets in Innsbruck, in what other cities might we market the
same gadgets?
If we have a Pubmed article of interest, what other articles should we read?
59
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 and members of
the Molecular function component of GO.
60
Evaluating the semantic approach?
The semantic approach is complicated, often produces ugly-looking
and slow results, and new tools emerge like Topsy . . .
. . . but it allows users to do some things more easily than they can be
done using the relational approach:
- Information stored in sentences is easier for (some) users to
understand, and extract relevant portions.
- Data merged with metadata makes metadata easy to find.
- Being sentence-based, SparQL may be more intuitive (and more
declarative?) than SQL, and may more easily support the use of
ontologies and inference.
- Distributed information is can be more easily utilized; users can
access multiple RDF documents in a single SparQL query, and even
browse distributed RDF sources as part of the LDW or GGG.
- Information resources can often be more easily integrated. Since no
unified storage schema is required, RDF versions of multiple
resources can be manipulated within the same triplestore, and
ontologies may be exploited in a more natural fashion.
- Some types of queries are much more easily composed than they
could be in SQL (Leipzig and Innsbruck).
61
Conclusions?
The usefulness of the semantic approach is difficult to evaluate, but it is safe to
say the relational model is not going away.
Use/value depends on who’s doing what, using what information, over what
platforms, and how usage patterns will vary over time (and they are!).
The semantic approach appears to be especially useful for integrating
information resources and for finding connections/relationships, but
integrating resources is not straightforward (see Satoo, et al. and Antezana,
et al. for examples), nor is quantifying connectivity.
You may need to differentiate between the semantic approach itself, and the
distributed capabilities of the Semantic Web. (Do the RDF warehouses
contradict the underlying intent to support distributed information resources?)
Where and how should metadata/semantics be injected into the “data stack”?
(caBIG does it differently.)
Where and how should ontologies be applied in information management?
(Using ontologies is mostly orthogonal to RDF proper, but see Renear, et al.)
What kinds of relational/semantic technology integrations are possible? Which
will prove synergistic?
If we have an RDF version of Wikipedia, can we have an ontology-enabled,
RDF version of Pubmed?
(Consider Enju and TexFlame.)
62
A long term role for the semantic approach?
This from the Oracle Semantic Technologies
Center (circa 2001!):
“By the end of this short paper, the reader should
understand the overall superiority of Semantic
Web technologies and be able to describe why it is
very likely that they will be embedded in the fabric
of nearly all data-intensive software within several
years.”
-- Jeff Pollock, Oracle Corporation
http://www.w3.org/2001/sw/sweo/public/BusinessC
ase/BusinessCase.pdf
63
A long term role in scholarly communication?
The Concept Web, according to their Web site:
“a dynamic, interactive fabric of concepts and their relationships. The
Concept Web is constructed from, inter alia, research literature,
Internet databases and other web sites together with off-line
resources.”
Mission of the Alliance:
“To enable an open collaborative environment to jointly address the
challenges associated with high volume scholarly and professional
data production, storage, interoperability and analyses for knowledge
discovery.“
Specific goals:
“The development and refinement of ways to capture information in
Semantically Rich Triples,” and to store, manage, and query such
information.
“The big issue we have here is this perverse situation in publishing, or
in formal scholarly communication, where researchers take data,
convert it into narrative form, and then employ really complex textmining tools based on complex natural language processing . . . to try
and turn this stuff into data again.” (Bilder, 2009)
64
For more information, see:
•
Antezana, Erik, et al., "BioGateway: a semantic systems biology tool for the life sciences”, BMC
Bioinformatics, 2009.
http://www.biomedcentral.com/1471-2105/10/S10/S11/abstract
•
Auer, Soren and Jens Lehmann, "What do Innsbruck and Leipzig have in common? Extracting
Semantics from Wiki Content”, European Semantic Web Conference (ESWC), 2007.
•
Bilder, Geoffry, Conceptweblog Conference Podcast, Concept Web Alliance Inaugural Meeting,
May 2009. http://conceptweblog.wordpress.com/conferences/
•
Bizer, Christian, Tom Heath, Tim Berners-Lee, “Linked Data--The story so far.”
http://tomheath.com/papers/bizer-heath-berners-lee-ijswis-linked-data.pdf
•
Herper, Matthew, Forbes Magazine, November 10, 2008.
http://www.forbes.com/forbes/2008/1110/090.html
•
Grobe, Michael, “RDF, Jena, SparQL, and the “Semantic Web”, SIGUCCS, 2009.
•
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
•
Nolin, Marc-Alexandre, et al., “Bio2RDF Network of Linked Data”, SWC submission, 2008.
http://www.cs.vu.nl/~pmika/swc-2008/Bio2RDF-Bio2RDF_submission.pdf
•
Sahoo, SS, et al., "An ontology-driven semantic mashup of gene and biological pathway
information: application to the domain of nicotine dependence", J Biomed Inform. 2008
Oct;41(5):752-65.
•
Renear, Allen H, et al., “Strategic Reading, Ontologies, and the Future of Scientific Publishing",
Science, 2009.
65