Towards Semantic Web engineering Multichannel publishing 3/12/2009 Olli Alm

Towards SemanticWeb engineeringMultichannel publishing 3/12/2009

Olli Alm

Outline

Part 1:Semantic WebOntologyRDF languagesQuerying and reasoning SW data

Part 2:Modelling SW dataSW data processingCase examplesSummary

Outline

Part 1

Outline: part 1

Semantic Web

• The vision: WWW with intelligent machines (Tim Berners-Lee)

• In practice: a set of languages and techniques for knowledge processing, modelling and representation

• W3C activity group: standards, specifications, recommendations, tools (www.w3.org)

http://www.w3.org/

Semantic Web

”The Semantic Web is about two things. It is about common formats for integration and combination of data drawn from diverse sources, where on the original Web mainly concentrated on the interchange of documents. It is also about language for recording how the data relates to real world objects. That allows a person, or a machine, to start off in one database, and then move through an unending set of databases which are connected not by wires but by being about the same thing.”(from W3C SW activity statement)

1) common formats for integration of data

2) for recording how the data relates to real world object

http://www.w3.org/2001/sw/

Semantic Web

The layer cake of the Semantic Web technologies

Semantic Web

• MVC & XML-movement in the web: separate the data model from it’s representation

• The Semantic Web:“unified” data model for representing (real world) data to be utilizedon any representation

what if we could…1. …represent any kind of (real world) data?2. …represent data in a unified way?3. …just take and reuse open data in our application?4. …integrate data easily from diverse sources?

Semantic Web

The Semantic Web:

• A branch of Artificial Intelligence?

• Symbolic AI: old ideas in a new form?

• Machine intelligence: symbolic representation of the facts

”Symbolic AI (or Classical AI) is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. facts and rules).”

Semantic Web

The Semantic Web:

• Explicit representation: an ontology

Semantic Web

The Semantic Web:

• Explicit representation: an ontology

• Not just explicit representation, in addition: shared

Semantic Web

The Semantic Web: shared conceptualization?

Semantic Web

The Semantic Web: shared conceptualization? (the linked data project)

Semantic Web

The Semantic Web: shared conceptualization

• everything is connected

• everything is referable (URIs)

• distributed set of statements (ontologies) as a basis of our world model

• ontology language(s): 1. tool for identifying resources2. tool for stating facts about resources (=statements)3. tool for sharing and integrating statements4. tool for reasoning the data

-e.g. acquiring new statements with deductive reasoning

• in SW world, term “ontology languages” refer to RDF-based languages such as RDFS, OWL (and OWL2).

Ontology

”OWL Full can be viewed as an extension of RDF, while OWL Lite and OWL DL can be viewed as extensions of a restricted view of RDF Every OWL (Lite, DL, Full) document is an RDF document, and every RDF document is an OWL Full document, but only some RDF documents will be a legal OWL Lite or OWL DL document”

RDF

An example of RDF-data (in XML serialization) -person info

<foaf:Person rdf:about="#me" xmlns:foaf="http://xmlns.com/foaf/0.1/">

<foaf:name>Dan Brickley</foaf:name> <foaf:homepage rdf:resource="http://danbri.org/" /> <foaf:img rdf:resource="/images/me.jpg" /> </foaf:Person>

RDF

An example of RDF-data (in TURTLE / TTL serialization) -person info

<http://mynamespace.fi#me> rdf:type foaf:Person ; foaf:name ”Dan Brickley” ; foaf:homepage http://danbri.org ; foaf:img http//danbri.org/images/me.jpg .

RDF

An example of RDF-data (in TURTLE / TTL serialization) -web page info

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>. @prefix dc: <http://purl.org/dc/elements/1.1/#>. @prefix exterms: <hhttp://www.example.org/terms/>.

<http://www.example.org/index.html> exterms:creation-date "August 16, 1999"; dc:language "en"; dc:creator

<http://www.example.org/staffid/85740>.

RDF

An example of RDF-data (graph representation) -web page info

RDF

An example of RDF-data (graph representation) -web page info

The graph-like nature of the RDF-resources / objects are nodes-properties / attributes are edges**properties are also resources (in the metalevel) and can be represented as a nodes in the graph (why is that?)

RDF

RDF (Resource Description Framework) is…-a statement language (logics)

-a statement = triple

A triple has three parts: 1) subject, 2) predicate and 3) object

Example from Friend-Of-A-Friend schema (FOAF)


subject

predicate

object

Triple says: ”me is a (type of) person”Triple says: ”me is called ”Dan Brickley”

Triple says: ”me has a homepage danbri.org”

The sets of triples forms a graph that interlinks resources with each other! (here: 4 triples, with subject #me)

http://mynamespace.fi/#me

http://danbri.org/

The sets of triples forms a graph that interlinks resources with each other!

RDF

URI• in RDF, everything has a unique identifier, URI• Uniform Resource Identifier• URI is an URL without link: not always clickable • in SW, URLs can be and are utilized as a URIs

(don’t mix with URNs, IRIs or PURLs)


Dan Brickley is identified by http://mynamespace.fi#me

foaf:name is an abbreviation for URI http://xmlns.com/foaf/0.1/(a property defined in foaf-namespace)

http://mynamespace.fi/#me

http://danbri.org/

http://xmlns.com/foaf/0.1/

RDF

URI• For consistency, URIs should not change often (or at all)• (should the URI change if the “identity” or “essence” of the resource

changes?)• URI identifies an object, but that doesn’t mean that different URIs refer to different resources:

in Web Ontology Language (OWL), we can state that two different URIs refer to the same object:

<rdf:Description rdf:about="#William_Jefferson_Clinton"><owl:sameAs rdf:resource="#BillClinton"/>

</rdf:Description>

(also the opposite is possible: we can state that two resources are distinct from each other)

RDFS

RDFS (Resource Description Framework Schema)• Divides the world into universals (classes) and particulars

(individuals / instances) TYPING

E.g. “Lassie is a dog” = @prefix sws: <http://www.metropolia.fi/~ollial/2009/11#>. <sws:lassie> rdf:type sws:dog ; foaf:name ”Lassie” .

Classes have subclasses: <sws:dog> rdf:type rdfs:Class ; rdfs:subClassOf sws:animal;

(Transitive) reasoning in RDFS:1) Lassie is a dog2) Dog is a kind of animal _ Lassie is a kind of animal

OWL

OWL (Ontology Web Language)

Extends RDFS to express• relations between classes, between instances

• property types: literal vs. objects literal property: foaf:name = “Olli” object property: foaf:knows http://someone/somewhere

• Subtyping of properties reasoning (e.g. functional, transitive)• Computability / complexity levels for the model• Three sublanguages OWL-FULL, OWL-DL, OWL-LITE

http://someone/somewhere

OWL

OWL (Ontology Web Language)

Extends RDFS to express• relations between classes, between instances

• property types: literal vs. objects literal property: foaf:name = “Olli” object property: foaf:knows http://someone/somewhere

• Subtyping of properties reasoning (e.g. functional, transitive)• Computability / complexity levels for the model• Three sublanguages OWL-FULL, OWL-DL, OWL-LITE

http://someone/somewhere

Reasoning in Symbolic AI

(Theory behind) ontology languages are (more or less) based on the assumptions that:

1) Logic is expressive (as a natural language): We can model our domain / world by defining a set of statements that holds (in our world). (state of affairs is the main concern, objects are secondary)

2) Language corresponds the world: If we are using strong and expressive language, we can model in a deep way real world phenomena in a consistent way and assume that our model corresponds the world.

3) Reason out the information: We can now deduce new (world) information (in the form of statements) by inferencing the set of statements.

Reasoning in Ontologies / open world

In addition to logic-as-a-language-correspondence-theories, the logicbehind ontologies follows the open-world semantics:• Our model may not contain all the relevant information• If something is stated, it is true, BUT• If something is not described, the machine don’t know the answer!

An example:

The statement in ontology: “Lassie is a dog”

A) The question: “Is Lassie a dog?”Closed world semantics: TRUEOpen world semantics: TRUE

B) The question “Is Lassie a cat?”Closed world semantics: FALSE Open world semantics: Don’t know

Practical reasoning in Ontologies

1) We load our data (e.g. the XML file) to the reasoning machine (e.g. Jena).

2) We set the inference engine on, and also define it’s level (e.g. reason out the transitive closures).

3) Now, we can ask statements from the model and get also the statements generated by the reasoner.

The data (1): “Lassie is a dog”, “Dog is a mammal”, “Mammal is an animal”Transitive closure inference (2):-reason out the is-a –relations, if there are related instances, add the new

facts for those instances.The deduced data (3):“Lassie is a dog”, “Dog is a mammal”, Mammal is an animal”, “Lassie is a

mammal”, “Lassie is an animal”


OWL: reasoning with properties

Transitive properties: P(x,y) AND P(y,z) P(x,z)An example:locatedIn(Punavuori,Helsinki) AND locatedIn(Helsinki, Uusimaa) locatedIn(Punavuori, Uusimaa)

Symmetric properties:P(x,y) P(y,x)An example:isFriendOf(Olli, Matti) isFriendOf(Matti, Olli)

Functional properties:P(x,y) AND P(x,z) y = z (~every object has it’s own unique value for P)An example:hasFather(Olli, Frank) AND hasFather(Olli, Paul) Frank = Paul


OWL: reasoning with properties

Transitive properties: P(x,y) AND P(y,z) P(x,z)An example:locatedIn(Punavuori,Helsinki) AND locatedIn(Helsinki, Uusimaa) locatedIn(Punavuori, Uusimaa)

Symmetric properties:P(x,y) P(y,x)An example:isFriendOf(Olli, Matti) isFriendOf(Matti, Olli)

Functional properties:P(x,y) AND P(x,z) y = z (~every object has it’s own unique value for P)An example:hasFather(Olli, Frank) AND hasFather(Olli, Paul) Frank = Paul

This means:We can define certain ”implication patterns” in ourmodel and utilize them for processing data.Instead of having only the ”static” data, new datais generated based on the ”implications”.

Reasoning and processing data

In addition to the inferencing in the model, we can processthe data in more traditional ways:

• Build a procedural program for processing data

• Use specific rule-language for processing

• Query the data by using specific RDF query language, e.g. SPARQL(RQL, RUL, RDQL, …)

• The best solution depends on the nature of the problem:e.g. the inference engine reasoning is usually expensive / costly

solution (=takes lot of time)

SparQL query language

SparQL: W3C recommendation

• Current de facto query language for RDF• Quite same as SQL to relational databases:

SELECT, WHERE, ORDER BY (why the FROM is missing?)

PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name ?mbox WHERE { ?x foaf:name ?name . ?x foaf:mbox ?mbox }


SparQL: W3C recommendation

• Current de facto query language for RDF• Quite same as SQL to relational databases:

SELECT, WHERE, ORDER BY (why the FROM is missing?)

PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX ns: <http://example.org/ns#> SELECT ?title ?price WHERE { ?x ns:price ?price . FILTER (?price < 30.5) ?x dc:title ?title . }


SparQL: why?

• Clear representation for data queries (instead of coding by hand)• Good query engine implementation fast data retrieval?• Implemented in many development libraries

What you cannot do with SparQL?

• Update data? (extension: SparQL Update)• Do recursive queries:

“get all the superclasses of the dog”

(procedural example)x = dogWhile(x has superclasses) {

add superclass to resultsetx = superclass

}

Part 2

Outline: part 2

Data modeling for the Semantic Web

When modelling things in ontologies, we can use “object-oriented” approach:

• Try to define the domain

• Model objects that exist in the domain and the relations between the objects

In the modelling task, we are defining

• The metadata schema as usual (~database schema / objects of the domain)

• In addition, we should also define the ‘domain ontologies’ or ‘domain vocabularies’ we are using


metadata schema

• Defines the primary objects (classes) to model: books, cars, persons, …

• Defines the properties for objects: title, author, edition, no of pages, ISBN, genre, …

• Properties either have literal values or object values• Literal / DatatypeProperty:

name, title, street address, isbn, hasGenre(?)

• Object property: hasFriend, isLocated, hasAuthor, hasGenre(?)

• For “similar” objects, you can use the inheritance (subclassing!)• woman is a person, person is an agent, agent is an entity…


metadata schema: defining properties for a class (in RDFS / OWL)

<myNS:book>rdf:type owl:Class .

<myNS:title>rdf:type owl:DatatypeProperty;rdfs:domain myNS:book;rdfs:range xsd:string.

<myNS:isbn> rdf:type owl:DatatypeProperty; rdfs:domain myNS:book; rdfs:range xsd:string .

<myNS:author>rdf:type owl:ObjectProperty;rdfs:domain myNS:book;rdfs:range myNS:author .

class definition

property definitions



rdfs:domain the objects that have this property

rdfs:range the suitable values for the property

• Ontology languages are “schemaless” in the sense that youcan assign any properties for any objects. (open world assumption)

• Reasoning on the rdfs:domain:

<myNS:hasTail>rdf:type owl:ObjectProperty ;domain: myNS:donkey .

<myNS:matti>rdf:type myNS:person ;myNS:hasTail myNS:tail001 .

<myNS:matti> rdf:type myNS:person ; rdf:type myNS:donkey ; myNS:hasTail myNS:tail001 .



rdfs:domain the objects that have this property

rdfs:range the suitable values for the property

• Ontology languages are “schemaless” in the sense that youcan assign any properties for any objects. (open world assumption)

• Reasoning on the rdfs:domain:

<myNS:hasTail>rdf:type owl:ObjectProperty ;domain: myNS:donkey .

<myNS:matti>rdf:type myNS:person ;myNS:hasTail myNS:tail001 .

<myNS:matti> rdf:type myNS:person ; rdf:type myNS:donkey ; myNS:hasTail myNS:tail001 .

”if it has the tail, it is a donkey!”


Domain vocabularies: reusing domain knowledge

• In our schema, we can refer to “external” ontologies thatdefine some domain of discourse.

• The idea: • you don’t have to reinvent the wheel• saves time and money• easy data integration (connected data)• (and you can always extend the domain vocabulary)

• In practice: 1) refer / fetch / download the ontology2) assign your schema properties (property range) to the values3) use the domain vocabulary to describe your resorces



• Case study: ONKI ontology service: www.yso.fi

• User interface, web services for utilizing domain vocabularies




Example domains:• Classification schemes• Geographical information (place+coordinate+relations)• YSO (General Finnish Upper Ontology – Yleinen Suomalainen

Ontologia)• DB-pedia (information extracted from the Wikipedia)• Author databases (Getty ULAN)



In addition to domain vocabularies, the reusageof schema definitions is also encouraged!

Why? allow data integration based on the properties existing metadata schemas may provide, well-thinked, mature solutions for modelling

Example schemas:• Dublin Core, simple DC• SKOS (for thesauri and concept scheme modelling)• FOAF (Friend-of-a-Friend: social connections)

Processing the Ontology data

• Although the RDF data may be initially distributed, (usually) it has to be stored in one place for reasoning / processing. ontology repositories, usually build on the RDMS.

(triple-stores, few big tables, attributes for subject, predicate and object) repositories are usually quite slow when compared to RDMS (WHY?)

• The RDF data (graph data) is strongly interconnected, the whole model has to be in memory or in DB for processing. e.g. usually streaming / SAX-like processing is not possible

• Many Semantic Web applications are concerned on processing or analyzing 1) subsumption hierarchies OR 2) connections between the resources

SW application domains

• Data integration based on the ontologies (e.g. Linked Data)

• Multifaceted, hierarchical search (e.g. Museum Finland / Museosuomi)

• Modelling and analyzing networked data (e.g. FOAF, Linked Data)

• Trust issues: who stated what? Who agreed? In which namespace? Based on what?

• Anything starting with ‘Semantic’: Semantic search, Semantic wiki, Semantic annotation, Semantic desktop, Semantic portal, Semantic repository, …

Ontology building

Protégé ontology editor

Linked data project

Museosuomi / Museum Finland

Kulttuurisampo / FoaF

http://www.kulttuurisampo.fi/ff.shtml

Semantic Web engineering

• Tools, models and languages for managing and processing distributed data

• Not just data: emphasis for modelling “real world knowledge”

• Reuse schemas, content and domain vocabularies

• Identify everything (URIs), make resources referable

• Networked, hierarchical, interlinked data

• Data processing with inference, rules, query language or procedural programming

• Open data?

• RDF: good for modelling / designing complex domains

Semantic Web engineering (problems)

Ontologies may complicate things• Versioning, modification of domain ontology, how the data utilizing the

ontology should react? • Who is responsible for maintaining the ontology (=expensive)? • Complex data model scrappy, low quality data• You can model the same things in simpler models, e.g. in SQL• Who needs URIs anyway?• Triplestores are usually slow, the level of abstraction in data index is

low (=triple)

-----------------------------------------------------------------------------There isn’t (really) such thing as

Semantic Web application development framework------------------------------------------------------------------------------

Further material

W3C Semantic Activity: http://www.w3.org/2001/sw/RDFS spec: http://www.w3.org/TR/rdf-schema/OWL spec: http://www.w3.org/TR/owl-ref/OWL2: http://www.w3.org/TR/2009/REC-owl2-overview-20091027/Wikipedia: semantic webLinked data: http://linkeddata.org/SKOS: http://www.w3.org/2004/02/skos/

Jena http://jena.sourceforge.net/ (for Java)RDFLib http://rdflib.net/ (for Python)

Reasoning:http://owl.man.ac.uk/2003/why/latest/#2http://www.w3.org/TR/2009/REC-owl2-primer-20091027/#Modeling_Knowledge:_Basic_Notions (in OWL2)

http://www.w3.org/2001/sw/

http://www.w3.org/TR/rdf-schema/

http://www.w3.org/TR/owl-ref/

http://linkeddata.org/



http://www.w3.org/2004/02/skos/

http://jena.sourceforge.net/



http://rdflib.net/

http://rdflib.net/

http://rdflib.net/

Thank you.Questions?

Documents

Towards Semantic Web engineering Multichannel publishing 3/12/2009 Olli Alm