Web3.0 seminar wipro-session2-logicalontological

24-06-2010

Web 3.0, Semantics & Session II –
Enterprise Computing Logical Ontological

Satish, Sukumar,
Feroz Sheikh & Venkatesh S
www.canopusconsulting.com June 2010

Objective
 Information interchange and modelling

 An introduction to semantic web technologies
 RDF, RDFS, OWL
2

 Semantic modelling
 Querying

 The web 3.0 technology stack

© Canopus Consulting

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The following Sessions will address:
 How do we build an application?
 How do we build the ontology?
 What are the key architecture components?
 What are the tools & technologies to use?
How do I choose which technology to use?
3



Semantic Web Application Lifecycle

Ontology Editors:
Protégé, TopBraid
Composer

4 Build Information Model

Semantic Query
Server

Refine/Evolve
Information Model
Create Assimilation Models
& Aggregate knowledge
RDF Stores:
Mulgara, Sesame Technologies:
GRDDL, RDFizers,
Programming: Jena OWLs, Automatic
Annotation

Retrieve and Use Semantic Data

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 Information Modelling
 Build Ontology (model level representation)

 Information Assimilation
 Populate Knowledgebase from various sources
5
 Including current applications
 Automatic Semantic Annotation of existing data
 Any type of document, multiple sources of documents

 Information Retrieval
 Applications: search, integrate/portal, summarize/
explain, analyse, decisions support
 Reasoning techniques: graph analysis, inferencing

Architecture Stack of Semantic Technologies

Application
HTTP SOAP

Programming API

Semantic Middleware
6 e.g. Semantic SOA SPARQL Processor Inference Engine

RDF-SQL
Adaptor

Relational
RDF Store
Store

Semantic Technology Stack


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Semantic Web Technologies

7

Source: W3C


The Perceptron.Net Use case
 A rich Cultural Informatics environment
designed to
 Create, Collect, Categorize any type of cultural
artifact – Music, Literature, Travel, Leisure,
8
Entertainment..
 Communities can be formed around content
 Make use of existing information on the network
and existing community infrastructure
 An example:
 Indian Music cannot be categorized along the
same lines as Western Music
 Genre, Album, Artist – is just not sufficient…

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The Perceptron.Net use case…
 Typical Queries we want to support:
 Thematic Album Creation Ability:
 Give me all songs that are directed by X, and music
composed by “y” and hero was “z”
9
 Give me all songs in Raga Kalyani – (must include
film, folk and classical songs)
 Give me all songs in Lord Rama in Sanskrit, which
are “stotras”…
 Give me all the recordings of live performacnes at
Sri Krishna Gana Sabha, Chennai


The Perceptron.Net use case…
 Provide an exploratory interface:
 Specify a generic criteria and successively filter until you find what you
need. E.g: specify a “mood” or a song you like and ask for “similar” songs
or songs that match such a mood.
 Allow community to add content, meta-data and find new
connections in the content.
10
 Content can be anywhere on the Internet
 Raaga.com, HamaraCd.com, MusicToday.Com, Orkut groups, blogs,
websites
 Not only music, but include content “about” music – articles, essays,
ratings, discussions – which should be used in connecting the
content, in searching the content, in enriching the content
 Provide feeds such that facebook type plug-in can be developed
easily – so that content and queries can be shared/updated from
anywhere.


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11


Practical Problem

12 LET US CREATE A COMPILATION OF
AMITABH BACHAN’S FILM SONGS


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We start with a music site
Or HamaraCD or India
Times Shopping or …

And we go through a fairly
exhaustive categorization
…

13
By actor
By album name
Release year


Or go to a site with a filmography

And drill down to some
of our favorite ones

14


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But what if we wanted to
 Get all songs for Amitabh Bachchan that
 Are from movies directed by Yash Chopra?

15


Or what if we wanted to

Get all songs of Amitabh
Bachan in which the
playback singer won an
award.

16


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We could do this
 By hand
 Make a list of movies from the filmography site
 Go to Raaga and get songs for those movies
 But what if there are multiple sources?
17


Across Multiple Sources

18


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The problem
 Implicit to Explicit
 Logic & domain knowledge is “Implicit” in an application
 In Code, DB Schema, Documentation etc.

 Humans can interpret it, but automated agents can’t

List<SongList>: getSongsInAlbum();
19
SongID Sequence Duration
Yaara Seeli 1 4:50
Seeli
Dekha Ek 2 2:55
Khwab toh
… … …

For agents, this logic needs to be “Explicitly” stated


To enable agents,
 Extract the Logic as Metadata
 The logic & domain knowledge is embedded in the application
 In object models, class hierarchies, instance names
 This needs to be extracted out, for others to link, access and use
 Deliver both data and metadata in a uniform manner
20


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We need
 Common Language
 Agents need a common language to interchange information

 Derive Conclusions
 Logic, that is now explicitly stated, can be used to draw conclusions

21
 Allow Independent Evolution
 Every application should be able to evolve independently
 Both at data level (new data) or schema level (new knowledge)

 Support Incremental Assimilation
 Different source can contain/provide different aspects of knowledge
 Community participation to evolve the knowledge
 Manifests itself in the principle of AAA (Anyone can say anything
about any topic)


Steps In Information Interchange
1. Map the various data onto an abstract data
representation
 Make the data independent of its internal
representation…
22  Expose it in a standard format
2. Merge the resulting representations to create
a single model
3. Make queries on the whole
 Queries that could not have been done on the
individual data sets


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Steps In Information Interchange

23


Enabling Technologies
 Semantic technologies (specifically RDF) treat
metadata as data and exchange both in exactly
the same way.
 They provide a way for anyone to make a basic
24 statement about anything and a means of
layering these statements into a model.


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Interchange Without APIs
 If the information provider can now produce
an RDF stream of data and metadata, the
assimilation agent can combine streams from
all sources and treat them as if they are from
25 the same source
 The only thing the agent needs to understand
the model of RDF, and not the individual
application APIs and models
 This is the fundamental premise of
information interchange on the web 3.0.


First Expose the Data
 As a set of relations
 Using RDF Jai Ho
p:title http://.../#JaiHo

p:movie

Slumdog…
p:composer

26

http://.../#ARR

p:name

AR Rehman

Song Data


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Second Merge Data from different Sources

p:title Same URI =
Same URI =
http://.../#JaiHo
Jai Ho Same
Same
p:movie Resource
Resource
Slumdog…
p:composer

27 http://.../#JaiHo

http://.../#ARR
p:sang

p:name http://.../#ARR

AR Rehman
http://.../#JaiHo
-ARR p:performed_at

p:music_director

Song Data http://.../#ARR
Performances Data


Merging Identical Resources

Jai Ho
p:movie

Slumdog…
p:composer

28 http://.../#JaiHo

http://.../#ARR
p:sang

p:name http://.../#ARR

AR Rehman
http://.../#JaiHo
-ARR p:performed_at

p:music_director

Song Data http://.../#ARR
Performances Data


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Third Query the data
 As if they are from the same source
 Answer questions that were not possible from either source
alone
 For example
 What is the title of the song sung by AR Rehman at Fireflies Music
29 Festival
Jai Ho
p:movie p:sang

Slumdog… http://.../#FFM
p:composer
http://.../#JaiHo
-ARR p:performed_at

http://.../#ARR
p:music_director

p:name

AR Rehman http://.../#ARR


Fourth Adding Additional Knowledge
 We can assert that the composer & music_director are same
 p:composer sameAs p:music_director
 Very handy for model transformations, language translations, de-
duplication etc.
Now we can ask queries not possible with either of the sources
“Composers” of songs played at Fireflies Music Festival

30

Jai Ho
p:movie p:sang

Slumdog… http://.../#ARR
p:composer
http://.../#JaiHo
-ARR p:performed_at

http://.../#ARR
p:music_director

p:name

AR Rehman http://.../#ARR


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Common Language

Reasoning & Inferences

Assimilation & Retrieval
RDF RDFS RDF DBs
OWL SPARQL

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RDF RDFS OWL SPARQL
• Resource • RDF Schema - • Web Ontology • Protocol and
Description Provides basic Language - RDF query
Framework - elements for adds language
defines the semantics to
structure to description of the schema
triples. RDF
vocabularies


Expressing Knowledge
 Explicit Knowledge – Semantic Models - Ontology
 Formal representation of the knowledge by a set of concepts
within a domain and the relationships between those concepts
 It is used to reason about the properties of that domain
 And may be used to describe the domain
32


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Mapping to Information Interchange

33

Source: W3C

Resource Descriptor Framework

34 WHAT IS RDF?


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Paradigms of Information: Spec for RDF
 AAA - Anyone can say Anything about Anything
 Consistency is not a necessary condition
 For example, source 1 – Yaara Sili Sili is a sad song
 Source 2 – Yaara Sili Sili is a famous song
 Community may provide music theory attributes to the song

35  To each their own
 No common schema, yet the ability to make globally (valid) statements
 For example, information about raagas can be combined with information
on film music

 There is always one more
 Open world assumption - facts can and are always incrementally added
 For example, one source may classify Lalbagh as a tourist place
 Another source may classify Lalbagh as a garden
 Application understands that gardens are suitable for “morning walk”


Sample Data

Song name Raaga Sung by Duration

Nanu palimpa Mohanam Dr 42 mins
Balamuralikrishna
Varuga varugave Mohanam MS 8 mins
Subbulakshmi
Kallalo Kannulalo Kalyani Leela 6 mins
36
Pranati Pranati Kalyani S P Balu 16 mins

Piluvukara Hindolam Ghantasala 6.3 mins
Alugukara


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Integrating Data from different sources
Source 1
Pranati Pranati Kalyani S P Balu 16 mins

Source 2
37 Kallalo Kannulalo Kalyani Leela 6 mins

Source 3 Varuga varugave Mohanam MS 8 mins
Subbulakshmi

Data distribution row by row – all participants must agree to the common schema


Interchange by Column
Source 2
Source 1 id Sung by
id Song name Raaga 1 Dr Balamuralikrishna
1 Nanu palimpa Mohanam 2 M S Subbulakshmi
2 Varuga varugave Mohanam 3 Leela
3 Kallalo Kannulalo Kalyani 4 S P Balu
38

4 Pranati Pranati Kalyani 5 Ghantasala

5 Piluvukara Hindolam
Alugukara

 Distributing by column
 Each participant must agree to the unique identifier


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Interchange by Cell
Source 1

Sung by

Row 1 Dr Balamuralikrishna

Source 2

39 Song name Source 3
Row 1 Nanu palimpa Raaga

Row 1 Mohanam

 Distributing by cell
 Each participant must agree to row ID & column name


The Cell
 Cell by cell division allows us to do just that
 Row ID and column needs to be identified

40

This is what RDF is -> TRIPLES of Data
Subject –> Predicate -> Object
Subject and Predicate have unique identifiers, object can be a literal or identifier

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RDF Expressions – Triples
 Triples - Subject, Predicate, Object
 Subject
 Must be a Resource
 Predicate
41  Must be a Resource
 Object
 Can be a Resource or a Literal
Resource
 <subject> has a property <predicate>, whose value is <object>
 A labelled connection between two resources
 E.g. Song: Jai Ho has a property composer whose value is A R Rahman
 E.g. MelakartaRaaga has a property NumberOfSwaras whose value is 7

Resource Resource Literal

Rules of RDF
 Global Uniqueness
 The RDF URI and names must be unique globally

 Sentence Form
 The order of knowledge representation in the sentence
42
should not change
 So the autonomous agents can consume that metadata

 Reuse
 If a document refers to an existing resource, then it is
talking about that same global resource


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Result
Anybody can say Every statement is an atomic RDF
Anything about Anything sentence

To each his own Every subject, object and predicate in RDF
is qualified
43

There is always one more RDF is a graph, no begin and no end,
An additional statement can always be
added to the graph


More rdf
 RDF blank nodes and their usage
 Identify an abstract concept – “there exists some”
 My ideal friend
 Id is always local to the document, need not be the
44 same
 Reification
 RDF collections
 Bag – unordered collection
 Seq – ordered collection
 Alt – unordered set of equivalent alternatives


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rdf:type
 Rdf:type is a property that provides an
elementary typing system
 song:yaara-seeli-seeli rdf:type
HindiSongs:FilmSong
45
 Does not make any assumption that
HindiSongs:FilmSong is a class – it is a resource
 Rdf does not have a definition for class


Bringing in the meaning

46 WHAT IS RDFS


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Need for RDF schemas
 First step towards the “extra knowledge”:
 define the terms we can use
 what restrictions apply
 what extra relationships are there?
47
 Officially: “RDF Vocabulary Description Language”
 the term “Schema” is retained for historical reasons…

Source: W3C

Classes, Resources
 RDFS defines resources and classes:
 everything in RDF is a “resource”
 “classes” are also resources, but they are also a
collection of possible resources (i.e., “individuals”)
48

 “composer”, “mood” are classes

 A R Rahman is an individual

 Love is an individual


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Classes, Resources (contd.)
 Relationships are defined among classes and
resources:
 “typing”: an individual belongs to a specific class
 “«A R Rahman» is a composer”
49
 to be more precise: “«http://.../#A R Rahman» is a
composer”
 “subclassing”: all instances of one are also the
instances of the other (“every novel is a fiction”)
 RDFS formalizes these notions in RDF


Classes, resources in RDF(S)
#artist

rdfs:subClassOf

http://perceptron.net/indianmusic#A R rdf:type
#composer
Rahman

50

 RDFS defines the meaning of these terms
 A resource may belong to several classes
 rdf:type is just a property…
 “«A R aRahman» is a composer and «composer» is an
«artist»”
 The type information may be very important for applications
 e.g., it may be used for a categorization of possible nodes


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Inferred Properties
#artist

rdfs:subClassOf

http://perceptron.net/indianmusic#A R rdf:type
#composer
Rahman

51

 is not in the original RDF data
 Can be inferred from the RDFS rules
 RDFS environments return that triple, too


Classes and Sub-Classes
 Challenge:
 We have instance data
about SuddaSwaras and <owl:Thing rdf:about="#ga">
<rdf:type rdf:resource ="#SuddaSwara"/>
VikritSwaras. </owl:Thing>

 We however often want <owl:Thing rdf:about="#ra">
52
<rdf:type rdf:resource ="#SuddaSwara"/>
to use Swaras to mean </owl:Thing>
both – for example, to
<owl:Thing rdf:about="#ni">
say that some Raaga has <rdf:type rdf:resource ="#VikritSwara"/>
</owl:Thing>
7 Swaras
 How do we state this?


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Classes and Sub classes
<rdfs:Class rdf:ID=“SuddaSwara">
<rdfs:subClassOf rdf:resource="#Swara"/>
</rdfs:Class>

 Class1 rdfs:subClassOf Class2
53
 Class1 is a specialization of Class2, membership in
Class1 implies membership in Class2, properties of
Class2 are inherited by Class1
 A class can be subClass of multiple classes
Class 1 subClassOf Class 2
Class 1 subClassOf Class 3

 Instances are specified using rdf:type

Inference – formal rules
 The RDF Semantics document has a list of (33)
entailment rules:
 “if such and such triples are in the graph, add this
and this”
54  do that recursively until the graph does not change


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Properties and Sub Properties
 Properties and classes are defined separately
from each other
 Property is not owned by any class
 Range and domain of properties can be specified
55  What type of resources serve as object and subject

 Sub Property
 Rdfs:subPropertyOf is used to define one property
as a sub-property of another
 The sub-property inherits the domain and range
definitions of the property

What does it look like

56

Data

Metadata

Inferred Assertions


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Properties , domains and ranges
 It is still rdf:Property not RDFS:Property, however RDFS adds the notion
of a domain and range to an rdf:Property

<rdf:Property rdf:ID=“directed_by">
<rdfs:domain rdf:resource=“#MusicDirector"/>
<rdfs:range rdf:resource="#FilmSongs"/>
57 </rdf:Property>
 Domain – what resources does the property apply to

 Range – what are the possible values

 If there are 2 domain or 2 range statements, it means both must be true
 Range can indicate:
 Rdf:resource => either a resource or a literal
 Rdfs:datatype


Inference based on Domains and Ranges
 Challenge – data typing based on use
 We have statements about songs and who composed them such as
 Endaro Mahanubhavulu isComposedBy Thyagaraja
 But we do not have a direct statement that says Thyagaraja, or any
one else is an instance of a class called Composers.
58
 Suppose we have to list all the composers in our model, what do we
do?

 This is a very common pattern in transformation rules


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Inference based on Domains and Ranges

 Answer:
 Define the range for isComposedBy to be of class Composers
 The RDFS inference will automatically deduce that anything
that is specified as the object of isComposedBy is an instance
of class Composer
59

Metadata

Inferred Assertion

Data

<owl:ObjectProperty rdf:about="#isComposedBy">
<rdfs:range rdf:resource="#Composer"/>
</owl:ObjectProperty>


Reinforcing the notion of “Schema”
 Domains and ranges are not used for
validation - but instead are used to determine
new information based on old information
 Does this surprise you?
60


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61 WHAT IS OWL


Ontologies
 RDFS is useful, but does not solve all possible
requirements
 Complex applications may want more
possibilities:
62
 characterization of properties
 identification of objects with different URIs
 Disjoint-ness or equivalence of classes
 construct classes, not only name them
 can a program reason about some terms? E.g.:
 “if «Person» resources «A» and «B» have the same
«email» property, then «A» and «B» are identical”


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Ontologies (Cont.)
 The term ontologies is used in this respect:
 “defines the concepts and relationships used
to describe and represent an area of
knowledge”
63  RDFS can be considered as a simple ontology
language
 Languages should be a compromise between
 rich semantics for meaningful applications
 feasibility, implementability


OWL - Web Ontology Language
 OWL is an extra layer, a bit like RDF Schemas
 own namespace, own terms
 it relies on RDF Schemas
 It is a separate recommendation
64


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OWL Overview
 OWL is a large set of additional terms
 For classes:
 owl:equivalentClass: two classes have the same individuals
 EXAMPLE – A:MISIC_DIRECTOR and B:COMPOSER
 owl:disjointWith: no individuals in common
For properties:
65

 owl:equivalentProperty
 EXAMPLE – A:VOCALS_BY and B:SINGER
 owl:propertyDisjointWith
 For individuals:
 owl:sameAs: two URIs refer to the same concept
(“individual”)
 owl:differentFrom: negation of owl:sameAs

Classes in OWL
 In RDFS, you can subclass existing classes…
that’s all
 In OWL, you can construct classes from
existing ones:
66
 enumerate its content
 through intersection, union, complement


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OWL: Class
 OWL:Class is a subset of RDFS:Class
 More expressiveness – restrictions, set operations …

 Owl:Thing
 Every resource that is an instance of a class is
automatically a member of OWL:Thing
67
 OWL:Nothing -> the empty class, most specialized

 Separation of classes and instances
 Though the language does not mandate it

 Classes contents can be enumerated
 The classes consists of exactly of those individuals

 Union of classes can be defined
 Other possibilities: complementOf, intersectionOf

OWL class definition

<owl:Class rdf:about="#Composer">
<rdfs:subClassOf rdf:resource="#Person"/>
</owl:Class>

68
<owl:Class rdf:about="#Composition">
<rdfs:subClassOf rdf:resource="&owl;Thing"/>
</owl:Class>


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Equivalence in OWL
 Equivalent Class
 If Class A equivalentClassOf Class B
 => they share the same members
 If x belongs to Class A then x also belongs to Class B and vice
versa
69
 Is equivalent to saying: A (Model) Design Pattern:

 A rdfs:subClassOf B How to implement
equivalence, when limited to
 B rdfs:subClassOf A RDFS vocabulary

 Equivalent properties
 If propertyA equivalentPropertyOf propertyB
 If propertyA applies between resources X and Y, then propertyB
also applies


OWL - exhaustive
 The combination of class constructions with
various restrictions is extremely powerful

 What we have so far follows same logic as before
 extend the basic RDF and RDFS possibilities with new
70
features
 define their semantics, ie, what they “mean” in terms of
relationships
 expect to infer new relationships based on those

 However, a full inference procedure is hard
 not implementable with simple rule engines, for example


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Properties
 owl:ObjectProperty is used to connect a resource to
another resource
 owl:DatatypePropery is used to connect a resource to an
rdfs:Literal (untyped) or an XML schema built-in data
type (typed) value
71
 Both Can have sub-properties

<owl:ObjectProperty rdf:about="#isComposedBy">
<rdfs:range rdf:resource="#Composer"/>
</owl:ObjectProperty>

<owl:DatatypeProperty rdf:about="#hasName">
<rdfs:range rdf:resource="&xsd;string"/>
</owl:DatatypeProperty>


More on properties
 Property can be the inverse of another
 Has child – has parent
 Usually seen in “containment” type
Inverse Property
associations
 Property can be symmetric ApB => BpA
72
 Knows, hasSpouse
 Usually abstract forms of more specialized
properties,
 Often you will find that most symmetric
properties are super-properties to others
 Property can be asymmetric
 hasMother, greaterThan, lesserThan
 Property can be transitive. Transitive & Symmetric
 isPartOf, contains
 Generally seen between entities of similar type

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More on properties
 Challenge
 We have the following statements:
 Raaga A isJanyaRaagaOf Raaga B
 Raaga C isJanyaRaagaOf Raaga D
73
 Raaga E isMelakarthaDerivative of Raaga A
 We get additional information that there is
something called JanakaRaaga such that if A is
JanyaRaaga of B then B is JanakaRaaga of A
 I want to:
 Get all JanakaRaaga of A
 Get all Raagas related to A


More on properties
 Answer
 isJanyaRaaga isInverseOf isJanakaRaaga
 isJanyaRaaga, isJanakaRaga, isMelakartaDerivative of
are sub-properties of raagaRelations
74

Entailed by OWL itself :

inverseOf is inverse of itself
In other words,
inverseOf is symmetric

It is not uncommon to find this pattern in model transformations


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Qualifying property membership
 Film songs are those songs that have appeared
in films

<owl:Class rdf:ID=“FilmSongs">
75 <rdfs:subClassOf rdf:resource="#Songs"/>
<rdfs:subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="#appearedIn"/>
<owl:someValuesFrom rdf:resource="#Films"/>
</owl:Restriction>
</rdfs:subClassOf>
</owl:Class>


Qualifying property membership
 owl:allValuesFrom
 If the property is used, then all values for the
property must belong to the class
 owl:someValuesFrom
76
 If the property is used, at least one of the values
must belong to the class
 owl:hasValue
 Of all the values a class has for a particular
property, at least one must be this specific value


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24-06-2010

Owl:cardinality
Likewise cardinality, and max cardinality

<owl:Class rdf:ID=“FilmSongs">
<rdfs:subClassOf rdf:resource="#Songs"/>
<rdfs:subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="#appearedIn"/>
77
<owl:minCardinality rdf:datatype=“XMLSchema#nonNegativeInteger">
1
</owl:minCardinality>
</rdfs:subClassOf>
</owl:Class>


SPARQL Protocol And RDF Query Language

78 QUERYING RDF


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Querying RDF
 A collection of rdf statements is a graph
 No root - no start no finish
 What is returned to a query is a graph
 a relational query forms a new table by combining
79
existing tables, an rdf query returns a new graph by
combining information from graphs from multiple
sources


Queries and their responses
 Querying an RDF store such as the one we
have just built will return exactly what is
asserted
 Example:
80
 Raaga:Sreeragam rdf:type Raaga:Ghana
 Raaga:Ghana rdfs:SubClassOf Raaga:CarnaticRaaga

A query such as ?x rdf:type Raaga:CarnaticRaaga
Will return nothing !!


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Retrieval models from RDF

Semantics
Semantic Query
Meaning of data & relationships (e.g SPARQL)

Internally represented as

81
Structure Graph Traversal
(e.g. Squish)
A graph of triples connected to each other

Serialized using

Syntax Syntactic Query
(e.g XPath/Xquery)
Formats such as XML/RDF or Turtle


Syntactic Level Query
<rdf:RDF
<Raaga rdf:about="#satmel">
<rdf:type rdf:resource="&owl;Thing"/>
<hasSwara rdf:resource="#ni"/>
<hasSwara rdf:resource="#nu"/>
<hasSwara rdf:resource="#pa"/>
<hasSwara rdf:resource="#ra"/>
<hasSwara rdf:resource="#sa"/>
</Raaga>
<owl:Thing rdf:about="#shuddhatodi">
82
<rdf:type rdf:resource="#Raaga"/>
<isMelakartaDerivative rdf:resource="#hanumatodi"/>
</owl:Thing>
for $r in document(music.owl)
where $r/Raaga/@rdf:about = ‘#satmel’
 Select a raga – (Xpath/XQuery) return $r/Raaga/hasSwara

 /RDF/Raga/@rdf:about=“#satmel”
 /RDF/Thing/@rdf:about=“#shuddhatodi”
 Limitations of this approach
 Can go out of hand very quickly
 Does not understand the semantics of RDFS & OWL
 Tied to the structure of RDF (which can be expressed in many ways)

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Structural Query
Subject Predicate Object

perceptron:satmel rdf:type perceptron:Raaga

perceptron:shuddhatoodi rdf:type perceptron:MelakarthaRaaga

perceptron:satmel perceptron:hasSwara perceptron:sa

perceptron:MelakarthaRaaaga owl:subClassOf perceptron:Raaga

…
83

 Possible queries
 Select * from Triples where Object = “owl:Raaga” and Predicate = “rdf:type”
 Select Predicate from Triples where Subject = “perceptron:satmel”
 Limitations of this approach
 Interprets any RDF model as just a set of Triples
 Does not understand the semantics of RDFS & OWL
 E.g. Looking for all raagas will fail here since shuddhatoodi is asserted to
be a MelakarthaRaaga, while it is a subClassOf Raaga is in the semantics of
the next triple

Querying at the Semantic Level
 Need a new language that can understand the semantics of RDFS & OWL

 Sample Queries
 Select ?x from <perceptron.net> where ?x <rdf:type> Raaga
 All MelakarthaRaagas will also be returned even though they are not explicitly
asserted to be a Raaga

84  Can draw inferences from the rules of RDFS and OWL
 Either computing and storing the closure of a given model
 Or Infer new statements as needed by the query on the fly

 Not tied to how the data is stored or serialized

 Special purpose query languages designed to facilitate this
 RQL, SPARQL, TQL
 SPARQL has emerged as the industry standard


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24-06-2010

Introducing SPARQL
 Designed to query collections of triples…
 …and to easily traverse relationships
 SQL-like syntax (SELECT, WHERE)
 Matches graph patterns
85


SPARQL – Key Characteristics
 Graph pattern matching ability
 Capability to restrict matches on a queried graph by providing a graph pattern
 Which consists of one or more RDF triple patterns, to be satisfied in a query

 Variable binding results
 Returns zero or more bindings of variables.
 Each set of bindings is one way that the query can be satisfied by the queried graph

86  Sub-graph results
 It must be possible for query results to be returned as a sub-graph of the original
graph

 Result limits
 Possible to specify an upper bound on the number of query results returned

 Streaming results
 Possible for the client to request that results be streamed

 WSDL support
 The protocol – including its interfaces, their operations, results, and types are
described using WSDL


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Sample Model Fragment

87

PREFIX perceptron: http://www.perceptronnetwork.com/ontologies/2010/...

SELECT ?raaga
WHERE {?raaga rdf:type perceptron:Raaga }


Let us take a closer look
PREFIX perceptron: http://www.perceptronnetwork.com/ontologies/2010/...

SELECT ?raaga
WHERE {?raaga rdf:type perceptron:Raaga }

 PREFIX
 Defines an alias for the namespace
88
 SELECT
 Select query
 FROM
 Optional clause, specifies the URI of the model
 Variables
 Marked by either ? or $
 WHERE
 Usually the most significant part of a SPARQL query
 Each triple is one condition (filter on the graph) (expressed using Turtle Syntax)


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SPARQL Select
 Challenge
 Number of Raagas is huge, paginate the results

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

SELECT DISTINCT ?s FROM <rmi://localhost/server1#perceptron>
89 WHERE {?s rdf:type perceptron:Raaga}
LIMIT 2 OFFSET 2
ORDER BY DESC(?s)

 DISTINCT – remove duplicate results
 LIMIT n – Limit the returned values to n rows
 OFFSET n – Offset the first n nodes
 Limit and Offset together can be used to paginate the results

 ORDER BY – To sort the results, can be ASC, DESC


Example of a Complex Query
 Challenge
 Search for the top 10 songs (by popularity) in my favorite Raaga

SELECT ?rendition

FROM <rmi://localhost/server1#perceptron>
FROM NAMED <my-preferences.rdf>
FROM NAMED <popularity.rdf>
90
WHERE {
GRAPH <my-preferences.rdf> {
?me dc:name “My Name" .
?me prefs:favouriteRaga ?fav_raga .
} .

?composition perceptron:hasRaaga ?fav_raga .
?rendition perceptron:hasComposition ?composition .

GRAPH <my-preferences.rdf> {
?rendition pop:popularity ?popularity
}
}
ORDER BY DESC[?popularity] LIMIT 10


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How this Query Would Work
My-favourites.rdf
perceptron.owl

“My Name”

dc:name Mohanam ?fav_raga

Person:XXX
prefs:favouriteRaaga perceptron:hasRaaga
91
?me

Chandracharita
?composition

pop:popularity
1 perceptron:isRenditionOf
1
1

?popularity rend-
chandracharita-
popularity.rdf bmk ?rendition


Additional SPARQL constructs
 INSERT
 INSERT DATA { triples block }
 DELETE
 DELETE DATA { triples block }
 OPTIONAL
 Used to define an optional condition
92
 UNION
 For alternate queries
 FILTER
 Value based filters
 ASK
 Returns true or false depending upon the condition
 DESCRIBE
 Returns the node graph for the given condition


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Technologies & Tools

93 SEMANTIC WEB INFRASTRUCTURE


Objective
 How do we build an application?
 How do we build the ontology?
 What are the key architecture components?
 What are the tools & technologies to use?
How do I choose which technology to use?
94



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24-06-2010

 Information Modelling
 Build Ontology (model level representation)

 Information Assimilation
 Populate Knowledgebase from various sources
95
 Including current applications
 Automatic Semantic Annotation of existing data
 Any type of document, multiple sources of documents

 Information Retrieval
 Applications: search, integrate/portal, summarize/
explain, analyse, decisions support
 Reasoning techniques: graph analysis, inferencing


Ontology Editors:
Protégé, TopBraid
Composer

96 Build Information Model

Semantic Query
Server

Refine/Evolve
Information Model
Create Assimilation Models
& Aggregate knowledge
RDF Stores:
Mulgara, Sesame Technologies:
GRDDL, RDFizers,
Programming: Jena OWLs, Automatic
Annotation

Retrieve and Use Semantic Data

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Information Modeling
 Information Model Consists of
 Description Component – Schema
 Designed by domain experts, community
 Description Base – Assertions, Extensions
 Automated agents who assimilate the information

97
 The model is an evolutionary process
 Start with a concept map or a taxonomy
 Leading up to a formal ontology
 It evolves over lifetime of the application

 Tools & Technologies
 Popular modeling tools – Protégé, TopBraid Composer, GrOWL
 Concept Mapping – CMAP Tools, CMAP Tools COE
 http://www.xml.com/2002/11/06/Ontology_Editor_Survey.html

RDF Stores, Triple Stores

98 PERSISTING RDF DATA


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Storage Models for RDF
Hand-crafted SQL,
 SQL Storage ORM

 Direct SQL access to RDF data
 Queries become complex as discussed earlier
 Can’t completely deal with the semantics of data

99  SQL Bridge Tools like – SDB,
Squirrel RDF
 Access via SPARQL interface
 Adaptor transforms SPARQL query into SQL
 Data stored in relational schema

 Native RDF Native non-relational
DB – such as
 Access via SPARQL interface Mulgara

 Data stored in native RDF databases


SQL Storage SQL Bridge Native RDF

Application Application Application

10
0
SPARQL SPARQL
SQL Queries
Queries Queries

Adaptor

Relational Relational Relational Native RDF
Schema Triples Triples Graph


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24-06-2010

SQL Storage SQL Bridge Native RDF

Application Application Application
Traditional RDF
approach Databases

10
1
SPARQL SPARQL
SQL Queries
Queries Queries

Adaptor

Relational Relational Relational Native RDF
Schema Triples Triples Graph


Storing RDF Data – Relational Model
 Relational Schema
 Data model consisting of well defined tables, columns and their meaning
 To provide flexibility, we would have to use techniques that dynamically
update the schema
 Still makes each row alike – whereas the fundamental premise of RDF is
that each row is potentially unique
10
2


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24-06-2010

Storing RDF Data – Triple forms
 Storing RDF predicates in a relational database
 Use the attributes as “extended properties”
 Simple triple forms (subject – predicate – object)
 Predicate tables (one table per predicate)
 Gives the requisite flexibility but makes indexing & retrieval difficult

10
3


Storing RDF Data –RDF Stores
 RDF Stores
 Provide a mechanism to store RDF data
 Provide a mechanism to query it using languages such as SPARQL
 Internally may or may not be based on relational databases
 Also known as Graph Databases or Schema-less Databases
10
 Although not all Graph databases support RDF
4

Jena SDB, TDB Oracle 11g RDF Database

Mulgara Semantic Store

OpenLink Virtuoso
AllegroGraph
OpenRDF Sesame


52

24-06-2010

Programming APIs, Technology Stack
10
5
PROGRAMMING FOR THE SEMANTIC WEB


Let us Revisit SPARQL
 SPARQL
 SPARQL Protocol and RDF Query Language
 It is both a Protocol and a Query Language

 Protocol Definition
10
6
 Defines a mechanism of invoking a query over the web
 Equivalent to a web service definition
 Defines one operation
 Query - with the input, output and fault definitions
 Defines the protocol bindings
 HTTP (get/post)
 SOAP (web service)
 An implementation may provide ANY of the above two

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Architecture Stack of Semantic Technologies

Application
HTTP SOAP

Programming API

Semantic Middleware
10 e.g. Semantic SOA SPARQL Processor Inference Engine
7

RDF-SQL
Adaptor

Relational
RDF Store
Store



RDF Programming Stack - Jena
 The most popular stack for Java is Jena (http://jena.sourceforge.net)
 Jena also has an in-memory graph manipulation API
 Jena APIs for RDF, RDFS, OWL is one of the most popular APIs
 It uses a Graph based model and pluggable architecture
 It allows plugging various storages, reasoners etc. to the API
10  Many RDF stores either have or are building support for Jena API
8

Jena uses a Graph model as the core
All reasoners also assert inferences into a Graph
Thus the output of a reasoner can be fed into another layer
It is a common pattern with Jena to do such layering


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Components of Jena Stack

Joseki HTTP SOAP

Jena API
Programming API
ARQ

10 SPARQL Processor Inference Engine
9

RDF-SQL
Adaptor

TDB
SDB
(Abstraction)
Relational
RDF Store
Store



Connecting to RDF Database – RDF Programming APIs
 RDF programming APIs are available in almost all major languages
 C, C++, C# and .Net, Haskell, Java, Javascript, Lisp,
 Obj-C, PHP, Perl, Prolog, Python, Ruby, Tcl/Tk

 There is no standard client API (yet!) - equivalent of JDBC
 Each RDF package has its own set of APIs
11  Most often they follow similar paradigms of Connections, Factories, Sessions
0

 The query syntax however is standardized
 Thus queries are portable across RDF stores
 At the same time, many packages have their own “dialects” of RDF query
languages

 Mulgara is one of the most popular open source RDF databases
 Has its own API to connect to the RDF Database
 In addition to SPARQL, has its own dialect (called iTQL)


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Connecting to Mulgara Database
URI SERVER_URI = URI.create("rmi://servername/instancename");
URI GRAPH_URI = URI.create("rmi://servername/instancename#model");
String query = "SELECT ?x WHERE { ?x <p:isDerivedFrom> <p:Kalyaani> }";

try {
// Creating a new connection from the factory using the server URI
ConnectionFactory factory = new ConnectionFactory();
Connection connection = factory.newConnection(SERVER_URI);
11 // Initialize the SPARQL interpreter with the graph URI
1 SparqlInterpreter interpreter = new SparqlInterpreter();
interpreter.setDefaultGraphUri(GRAPH_URI);
// Parse and execute the query on the connection
Query query = interpreter.parseQuery(queryStr);
Answer a = connection.execute(query);
// Use the results
RdfXmlEmitter.writeRdfXml((GraphAnswer) a, System.out); Sample Java
} code to connect
catch (Exception e) { to Mulgara
e.printStackTrace(); Database using
} Mulgara Client
finally { API
connection.dispose();
}


Adopting Existing SQL Databases

11
2

Source: Tim Berners-Lee


56

24-06-2010


11
3

Source: W3C


11
4
APPENDIX


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24-06-2010

RDF Triples
 Resources can use any URI, e.g.:
 http://www.example.org/file.xml#element(home)
 http://www.example.org/file.html#home
 http://www.example.org/file2.xml#xpath1(//q[@a=b])
11
5
 URI-s can also denote non Web entities:
 http://www.ivan-herman.net/me is me
 not my home page, not my publication list, but me
 RDF triples form a directed, labelled graph


RDF Primitives
 Resources
 Anything that can be uniquely identified
 Using a URI
 E.g. http://www.perceptron.net/indianmusic#A R Rahman

11
Namespace Fragment ID
6

 Properties
 Are resources in themselves

 Literals
RDF is a
 Strings general
model for
 With optional data types triples


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OWL Full
 No constraints on any of the constructs
 owl:Class is just syntactic sugar for rdfs:Class
 owl:Thing is equivalent to rdfs:Resource
 this means that:
11
7  Class can also be an individual, a URI can denote a
property as well as a Class
 e.g., it is possible to talk about class of classes, apply properties
on them

 Extension of RDFS in all respects
 But: no system may exist that infers
everything one might expect


OWL Full usage
 Nevertheless OWL Full is essential
 it gives a generic framework to express many things
 some application just need to express and interchange
terms
11
8  Applications may control what terms are used
and how
 in fact, they may define their own sub-language via,
eg, a vocabulary
 thereby ensuring a manageable inference procedure


59

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