ihme course spring 2015 - jyväskylän...

UNIVERSITY OF JYVÄSKYLÄ

Semantic Web and Linked Data

IHME courseSpring 2015

University of Jyväskylä Khriyenko Oleksiy


Evolution of the Web

2

CONTEXT

OWLOWL

Communication:Human-to-Data,

Human-to-Service, Human-to-Human,

Service-to-Data, Service-to-Service.

Resources:Static Data,

Static Services.

Static Environment

Proactive Goal-driven Resources:

Communication:

data, services/software, processes,organizations, real world objects (human,device, machine, etc.)

Resource-to-Resource (Thing-to-Thing).

Context-aware Flexible Interoperable Dynamic Collaborative Environment

Communication:Human-to-Data,

Human-to-Service, Human-to-Human,

Service-to-Data, Service-to-Service.

Resources:Static Annotated Data,

Ontology-driven Services/Software.

Flexible Interoperable Static Environment

Collaborative Environment

OWLOWL

Web of Data Web of ServicesInternet of Things

Web of People

10/04/2015 IHME course


3

Avalanche of informationEducation

Events (art, cultural, sport), exhibitions, etc.

Work

Public transport scheduling systemShopping Centers:

sales, offers, etc.

Emergency service activities

Social Networks

Road and city services

Weather forecast and disaster

notification systems

But!!!Information is not

yet a data…



Demands and Challenges

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� society requires new innovative services and applications that make the life much easier, comfortable andinteractive;

� industry requires new intelligent systems to better perform maintenance and do better automation of productdevelopment and product operation processes;

� markets are looking for new opportunities based on information and data co-creation and reuse.

Demands of Society and Businesses

� unavailability of data limits us to develop new useful service and whittles away context-awareness of applicationsand services;

� complex accessibility and heterogeneity of data sources limits consumption of data by applications and services;

� human orientation of data formats slows down the process of intelligent autonomous service creation and serviceintegration;

� passiveness of data sources, lack of handy channels to provide and manage data, minimizes process of datareuse.

DataChallenges

� being bounded to certain data source, application is limited with possibility to access other data sources, to getmore fresh and updated information;

� being based on limited (closed) data model, application is not able to utilize data produced by another applicationand be interoperable.

Applications and Services



World Wide Web

� AAA principle = Anybody can writeAnything about Any topic

� Basic building block is a web page� Any web page can refer to any other

web page freely

� No central point of control

� No central repository => Documentsscattered across the whole Web

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� Problem : Web page is a document for humans . For computers (machines) web pages are too difficult to understand



Semantic Web vision

� Solution– Let’s produce Web data in a form that is easy to

“digest” by a machine without losing good propertiesof WWW

� How?– Switch: informal representation => formal model– Connect information, but stay consistent– Distribute information (no central repository)

� Semantics– Relation between signs, words, symbols and the

things (documents, people, places, events,organizations, concepts, etc.) to which they refer.

– Relation of the things to each other.

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I am enjoying about learning and using new technology…

Syntax:

Semantics:

I technologyI love technology____

Web

Semantic Web

RDF

RDF

RDF

RDF

From decentralized platform for

distributed presentationstowards decentralized platform for

distributed knowledge



Disambiguity of data

� Disambiguity of referencing to things:

� Disambiguity of conceptso Example:

• John is attracted to Mary. • North pole is attracted to south pole.

o There has to be some common understanding of the domain in questiono Solution: Ontology – a precise explanation of terms and reasoning in a subject area.

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Computer symbolsIdeas Human symbols

mouse

cry

mouse1

mouse2

cry1

cry2

bank 1

bank 2

bank

o Example: mouse, windows, bank, cry, etc.o Every thing should have its unique nameo Solution: URIs (Uniform Resource Identifiers)

Usually URIs are represented in a form of URLs:• http://www.jyu.fi/people/students/john/assignments/assignment1• http://www.jyu.fi/people/students/john/assignments/assignment2• http://www.jyu.fi/people/students/john/assignments/assignment3

Namespace as a prefix of the short (qualified name):Full name: http://www.jyu.fi/people/students/john/assignments/assignment1

Use qualified names (qnames):as:assignment1, as:assignment2, as:assignment3

Prefix (for example as:) Rest of the name



RDF (Resource Description Framework)

� RDF is a general method to decompose knowledge into small pieces with rulesabout the meaning of those pieces. It is a method to describe facts in a short form.

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ID Firstname Surname City

145 Albert Einstein NY

626 Marie Currie LA

California

State

USACountry

Person-145

Albert

Firstname

Einstein

NY145ID

Surname

City

Marie Currie

LA626

Person-626

ID

Firstname

Surname

City

� RDF represents graphs

� Everything is a Resource– Anything that we can talk about and

has identity in a form of URI.– Example: human, building, weather



RDF as graph, RDF as text

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AlbertEinstein

c:NY

145

p:per145

f:hasID

f:hasFirstname

f:hasSurname

f:livesIn

p:per145 f:hasID “145” . p:per145 f:hasFirstName “Albert” . p:per145 f:hasSurname “Einstein” . p:per145 f:livesIn c:NY .

All the data in RDF is described in statements/triples:subject – predicate – object



Graph matching and merging

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Albert Einstein

c:NY

145

p:per145

f:hasID

f:hasFirstname

f:hasSurname

f:livesIn

The Big Apple

s:NY

8 175 133

c:NY

c:citizens

c:nickname

c:state

Albert Einstein

145

p:per145

f:hasID

f:hasFirstname

f:hasSurname

f:livesIn

The Big Apple

s:NY

8 175 133

c:NY

c:citizens

c:nickname

c:state

c:NY

f:livesIn



Serialization

� The way of representing the graph in textual form� Serializations (notations):

– RDF/XML

– TriX– N-triples– Turtle (Terse RDF Triple Language)– Notation 3



RDF/XML

� Suitable for machines� Many XML parsers exist� Difficult for humans to see subject-predicate-object triples

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<rdf:RDFxmlns="http://data.gov/ontology/edu#" xmlns:log="http://www.w3.org/2000/10/swap/log#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax- ns#">

<rdf:Description rdf:about=" http://www.jyu.fi/courses/TIES452 "><credits >5</ credits >

</rdf:Description> <rdf:Description rdf:about=" http://www.jyu.fi/people/Mary ">

<studies rdf:resource=" http://www.jyu.fi/courses/TIES452 "/> <livesIn xmlns="http://data.gov/ontology/urban#"

rdf:resource=" http://www.geo.com/city/Turku "/> </rdf:Description>

</rdf:RDF>



N-triples

� Simple textual serialization of RDF statements� Each statement consists of subject, predicate and object

separated by a white space� Statements are separated by dots (.)� Resources are referred to with full URIs in <> brackets� Literals are wrapped into double quotes (“ ” )

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<http://www.jyu.fi/people/Mary> <http://data.gov/ontology/urban#livesIn> <http://www.geo.com/city/Turku> .

<http://www.jyu.fi/people/Mary> <http://data.gov/ontology/edu# studies > <http://www.jyu.fi/courses/TIES452> .

<http://www.jyu.fi/courses/TIES452> <http://data.gov/ontology/edu#credits> “5” .



Turtle

� Same example as before:

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@prefix p: <http://www.jyu.fi/people/> .@prefix u: <http://data.gov/ontology/urban#> .@prefix edu: <http://data.gov/ontology/edu#> .@prefix co: <http://www.jyu.fi/courses/> .@prefix ci: <http://www.geo.com/city/> .

p:Mary u:livesIn ci:Turku .p:Mary edu:studies co:TIES452 .co:TIES452 edu:credits “5” .

p:Mary u:livesIn ci:Turku ; edu:studies co:TIES452 .co:TIES452 edu:credits “5” .

<http://www.jyu.fi/people/Mary> <http://data.gov/ontology/urban#livesIn> <http://www.geo.com/city/Turku> .

<http://www.jyu.fi/people/Mary> <http://data.gov/ontology/edu# studies > <http://www.jyu.fi/courses/TIES452> .

<http://www.jyu.fi/courses/TIES452> <http://data.gov/ontology/edu#credits> “5” .



Manipulation of RDF data

� Storing– RDF file on the web– Specialized RDF storage (“RDF database”)– Other form (*.xls, DB, …) exposed as RDF

� Querying– Like in relational DB there is a query language (SPARQL)– Can query from several sources (web sources, local RDF storages, etc.)

� Reasoning– Can derive new facts from already existing facts– Can check consistency of the model– Does not exist in relational DB !!!



Storing of RDF data

� Small datasets (few triples)– RDF file published on the web or stored locallyExamples: *.rdf , *.nt , *.ttl , *.n3 , etc.

� Large datasets (thousands to millions of triples)– Database-bases solution better. Usually in form of RDF storageExamples: • Native RDF Stores (4/5Store , AllegroGraph , Apache Jena TDB , Mulgara , GraphDB™ , etc.)• DBMS-backed Stores (ARC2, Apache Jena SDB , Oracle Spatial and Graph , Semantics Platform ,

RDFLib , etc.)• Hybrid Stores ()• Non-RDF DB support (D2RQ Platform )

� Legacy data– Keep in original form– Provide mapping to RDF– Expose as RDF to the outer world

List of Triplestores: http://en.wikipedia.org/wiki/Triplestore



Querying of RDF data

� SPARQL- is an RDF query language, that is, a semantic query language for databases,able to retrieve and manipulate data stored in RDF format.SPARQL query general form:

� SPARQL 1.1 Update (SPARUL or SPARQL/Update) – is a declarative datamanipulation language that is an extension to the SPARQL query language and provides theability to insert, delete and update RDF data (as well as manipulation with graphs) held withina triple or quad stores.

Useful links: http://www.w3.org/TR/rdf-sparql-query/

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

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PREFIX (Namespace Prefixes)e.g. PREFIX f: <http://example.org#>

SELECT (Result Set)e.g. SELECT ?age

FROM(Data Set)e.g. FROM <http://users.jyu.fi/~olkhriye/itks544/rdf/people.rdf>

WHERE(Query Triple Pattern)e.g. WHERE { f:mary f:age ?age }

ORDER BY, DISTINCT, etc. (Modifiers)e.g. ORDER BY ?age



Simple SPARQL query

� Show me all things that are loved. Also show me their age (f:age )

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f:mary

25

f:john

f:age

f:janef:billf:loves

30

24

26

f:age

f:loves

f:age

f:age

Data Query

PREFIX f: <http://example.org#> SELECT ?person ?ageWHERE {

?x f:loves ?person . ?person f:age ?age

}

person age

f:jane 26

f:mary 24

Result

?person

?age

f:age

?xf:loves



Ontologies

� Ontologies are formal models that describe a certain domain (e.g. medical ontology, IT

ontology, milk ontology, etc.) and specify the definitions of terms by describing their relationshipswith other terms in the ontology. Consists of:o TBox - describes abstract concepts and their relationships, taxonomy, classification;o ABox - describes concrete individuals and their relationships to other individuals and/or abstract

concepts from Tbox.

� Class (type)o Represents a set of things that share same properties (and/or behavior)o Example: Person, Fruit, Feeling, etc.

� Instance (individual)o Represents a concrete thingo Can belong to one or more classeso Example: johnDoe, appleGoldenDelicious, anger, etc.

There cannot be a global ontology of everythingo Ontologies are dynamic (they change in time)o Every person can have a different perspective on the domain

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@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .@prefix ont: <http://www.john.com/myOntology.owl#> .

ont:benny rdf:type ont:Dog .ont:superman rdf:type ont:ComicBookCharacter .ont:mrBean rdf:type ont:ComicCharacter .



Ontology language

� Language that is used to formally define Ontologies� Example:

o RDFS (RDF Schema) - simple ontology languageo OWL (Web Ontology Language) - has more expressive power than RDF Schema

providing additional vocabularyo OWL2 is extension of OWL

� Majority based on RDF model as wello Ontology written in such language is RDF itself

� Differences between ontology languageso Expressivenesso Computational complexity of reasoning

� Protégé is an ontology editor



RDF Schema (RDFS)

� Simple ontology language (W3C Recommendation in 2004)� Prefix: � Features:

– Declaration of classes and subclass hierarchy:

– Declaration of literals and their hierarchy:

– Definition of properties and their hierarchy:

– Other features (statement, container, collections, comments, etc.)

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@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema #>

x:Human rdf:type rdfs:Class .x:Human rdfs:subClassOf x:LivingBeing .

x:Henkilotunnus rdf:type rdfs:Literal .rdfs:Datatype rdfs:subClassOf rdfs:Literal .

x:hasAge rdf:type rdf:Property .x:hasAge rdfs:domain x:LivingBeing .x:hasAge rdfs:range xsd:int .rdfs:subPropertyOf rdf:type rdf:Property .x:hasMovablePart rdfs:subPropertyOf x:hasPart .x:hasStaticPart rdfs:subPropertyOf x:hasPart .



RDFS example

� Ontology

� Annotated resource

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@prefix x: <http://mypage.com/myOntologies/humanOntology#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

x:LivingBeing rdf:type rdfs:Class .x:Human rdf:type rdfs:Class ;

rdfs:subClassOf x:LivingBeing .x:hasAge rdf:type rdf:Property ;

rdfs:domain x:Human ;rdfs:range xsd:int .

@prefix x: <http://mypage.com/myOntologies/humanOntology#> . @prefix xsd: <http://www.w3.org/2000/01/rdf-schema#> .

x:bill rdf:type x:Human ; x:hasAge "40"^^xsd:int .

@prefix x: <http://mypage.com/myOntologies/humanOntology#> . @prefix xsd: <http://www.w3.org/2000/01/rdf-schema#> .

x:bill rdf:type x:LivingBeing ; "40"^^xsd:int .x:hasAgex:hasAge



OWL example

� Value constraint: owl:hasValue

� Value constraint: owl:someValuesFrom

:CitizenOfJyvaskyla rdf:type owl:Class ;owl:equivalentClass [

rdf:type owl:Restriction ;owl:onProperty :livesInCity ;owl:hasValue :cityJKL

] .

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:FinnByOrigin rdf:type owl:Class ;owl:equivalentClass [

rdf:type owl:Restriction ;owl:onProperty :hasParent ;owl:someValuesFrom :Finn

] .



OWL example

24

� Cardinality constraints::Mammal rdf:type owl:Class;

rdfs:subClassOf [rdf:type owl:Restriction;owl:onProperty :hasParent;owl:cardinality 2

];rdfs:subClassOf [

rdf:type owl:Restriction;owl:qualifiedCardinality 1;owl:onProperty :hasParent;owl:onClass :Female

];rdfs:subClassOf [

rdf:type owl:Restriction;owl:qualifiedCardinality 1;owl:onProperty :hasParent;owl:onClass :Male

].



OWL example

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� owl:FunctionalPropertyExample: ex:marriedTo (in monogamous cultures);

� ��

�

�

��

� owl:inverseOfExample: ex:isOwnedBy & ex:owns, ex:hasChild & ex:hasParent are inverse.

� �

��

��

� owl:SymmetricPropertyExample: ex:hasSpouse. � �

�

�

��

�

� owl:TransitivePropertyExample: ex:bossOf, ex:hasAncestor.

� ...



Reasoning on top of RDF data

� Ontology-based reasoningo The inference rules for RDF-S or OWL are fixed. Therefore: No need for rule engine,

procedural algorithm is sufficient

� Rule-based reasoning usually requires:o A language for representing the ruleso A rule engine

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:John :hasWife :Mary :John rdf:type :Human . :John rdf:type :Man . :Mary rdf:type :Human . :Mary rdf:type :Woman . :Mary :hasHusband :John.

Family ontology

+ also means

:Mary :hasHusband :John

REASONER(RULE ENGINE)

:John :hasWife :Mary (?a :hasWife ?b) => (?b :hasHusband ?a)

Belief

Rule

Premise(s) Conclusion(s)if , then

Inferred Belief Inverse property

� �

��

��



Some rules of RDF Schema

� If a resource is an instance of a class, it is also an instance of anysuper-class of that class (any human is a mammal).

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� If a statement with a property is made, the statement with anysuper-property is also true (if you love something, you also like it).

:Mammal rdf:type owl:Class.:Human rdf:type owl:Class.:Human rdfs:subClassOf :Mammal.:John rdf:type :Human.

:John rdf:type :Mammal.also means

:like rdf:type owl:ObjectProperty.:love rdf:type owl:ObjectProperty.:love rdfs:subPropertyOf :like.:John :love :Mary .

:John :like :Mary .also means




� Transitive property:– If class A is a sub-class of B, while B is a sub-class of C, then A is a

sub-class of C (mother is woman, woman is human, therefore mother ishuman).

– Also applies to sub-properties� Example: rdfs:subClassOf and rdfs:subPropertyOf are transitive properties

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:Human rdf:type owl:Class.:Woman rdf:type owl:Class.:Mother rdf:type owl:Class.:Woman rdfs:subClassOf :Human.:Mother rdfs:subClassOf :Woman.:prefer rdf:type owl:ObjectProperty.:like rdf:type owl:ObjectProperty.:love rdf:type owl:ObjectProperty.:like rdfs:subPropertyOf :prefer.:love rdfs:subPropertyOf :like.

:Mother rdfs:subClassOf :Human.:love rdfs:subPropertyOf :prefer .

also means

��

�




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� If a property is defined to have class A as its domain, and astatement with that property is made, the subject of the statementmust be an instance of A.– The same for the range of a property and the object of a statement.

:Human rdf:type owl:Class .:love rdf:type owl:ObjectProperty ;

rdfs:domain :Human ;rdfs:range :Human .

:John :love :Mary .

:John rdf:type :Human .:Mary rdf:type :Human .

also means



Some rules of OWL

� Some of the property characteristics allow reasoners to infer newknowledge about instances and their relations:

– owl:inverseOf � �

��

��

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:Human rdf:type owl:Class .:hasChild rdf:type owl:ObjectProperty .:hasParent rdf:type owl:ObjectProperty .:hasChild owl:inverseOf :hasParent .:John rdf:type :Human .:Mary rdf:type :Human .:John :hasChild :Mary .

:Mary :hasParent :John .also means



Rule-based reasoning

� The OWL language is not able to express all relations (ex: it cannot express the relation “child of married parents“).

� The expressivity of OWL can be extended by adding rules to an ontology.

� Need for rule definition language:– SWRL (Semantic Web Rule Language)– Notation 3 (N3) logic– RIF (Rule Interchange Format)



Example@prefix rdf: <http://www.w3.org/1999/02/22-rdf-synt ax-ns#> .@prefix owl: <http://www.w3.org/2002/07/owl#> .

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:Human rdf:type owl:Class .:dan rdf:type :Human .:peter rdf:type :Human .:mary rdf:type :Human .:jon rdf:type :Human .:betty rdf:type :Human .

:ancestorOf rdf:type owl:TransitiveProperty.:hasSpouse rdf:type owl:SymmetricProperty.:brotherOf rdf:type owl:ObjectProperty.:sisterOf rdf:type owl:ObjectProperty.owl:inverseOf rdf:type owl:SymmetricProperty .

:brotherOf owl:inverseOf :sisterOf .

:dan :ancestorOf :peter .:peter :ancestorOf :jon .:peter :hasSpouse :mary .:betty :sisterOf :jon .

{ ?P rdf:type owl:SymmetricProperty . ?S ?P ?O

} => {?O ?P ?S} .

{ ?P owl:inverseOf ?Q .?S ?P ?O

} => {?O ?Q ?S} .

{ ?P rdf:type owl:TransitiveProperty .?S ?P ?X .?X ?P ?O

} => {?S ?P ?O} .

+

Rules

:dan :ancestorOf :jon .:mary :hasSpouse :peter .:sisterOf owl:inverseOf :brotherOf .

:jon :brotherOf :betty .

Data



Machine readable data exchange

� RDFa– Resource Description Framework in attributes (W3C Recommendation).

It is a domain-independent way to explicitly embed RDF data in attributesof a web page to:

– transfer data from an application to another through the web;– write data only once for web users and web applications.

� JSON-LD - JavaScript Object Notation for Linked Data. Extension ofJSON - simple property-value type machine readable data exchangeformat



RDFa

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<div vocab=“http://schema.org/” prefix=“ex: http://example.com/” resource="ex:alice/posts/trouble_with_bob“typeof=“Article”>

<h2 property="title"> The trouble with Bob </h2>...

The trouble with Bob is that he takes much better photos than I do:...

<div resource="ex:bob/photos/sunset.jpg"prefix=“dc: http://purl.org/dc/terms/” >

<img src="http://example.com/bob/photos/sunset.jpg" /><span property=“title"> Beautiful Sunset </span>by <span property=“dc:creator"> Bob</span>.

</div></div> The trouble with Bob

…The trouble with Bob is that he takes much better photos than I do:…

Beautiful Sunset by Bob



RDFa

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<div vocab=“http://schema.org/” prefix=“ ex: http://example.com/ ” resource=" ex:alice/posts/trouble_with_bob “typeof=“ Article ”>

<h2 property=" title "> The trouble with Bob </h2>...

The trouble with Bob is that he takes much better photos than I do:...

<div resource=" ex:bob/photos/sunset.jpg "prefix=“ dc: http://purl.org/dc/terms/ ” >

<img src="http://example.com/bob/photos/sunset.jpg" /><span property=“ title "> Beautiful Sunset </span>by <span property=“ dc:creator "> Bob</span>.

</div></div>

@prefix sc: < http://schema.org/ > .@prefix ex: < http://example.com/ > .@prefix dc: < http://purl.org/dc/terms/ > .

ex:alice/posts/trouble_with_bob a sc:Article ; sc:title ” The trouble with Bob ” .ex:bob/photos/sunset.jpg sc:title ” Beautiful Sunset ” ; dc:creator “ Bob” .



Semantic Annotation Tools

� OpenCalais� Zemanta� DBpedia Spotlight� OnTeA� RDFaCE� Structured Data Markup Helper� FRED� Semantator� Etc.



Linked Data

� In 2006, Tim Berners-Lee set out four simple principles for publishing data on the web. (http://linkeddata.org)

– Use URIs to identify things.– Use HTTP URIs so that people can look up those names.– When someone looks up a URI, provide useful information, using

the standards (RDF, RDFS, SPARQL).– Include links to other URIs, so that they can discover more things.

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2007

2011

volume of data has grown fromaround 2 billion triples in 2007to over 30 billion in 2011…

2014

In 2014, altogether, the diagramcontains 570 datasets and 2909linkage relationships between thedatasets...



Linked Open Data

� In 2010, Tim Berners-Lee suggested a 5 star deployment scheme for Open Datato encourage people (especially government data owners) to improve linked data.

� Linked Open Data (LOD) is Linked Data which is released under an open license, which does not impede its reuse for free. LOD2 - http://lod2.eu/Welcome.html

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Available on the web(whatever format) butwith an open license, tobe Open Data.

Available as machine-readablestructured data (e.g. excel instead ofimage scan of a table).

The data does not use a proprietary format (e.g.CSV instead of excel).

All the previous plus, data use only open standards from W3C(RDF and SPARQL) to identify things, so that people can point atyour stuff.

All the before, plus: Link your data to other people’s data to provide context.



� Reusing existing well-known vocabularies. In order to make it possible for client applications toprocess your data, you should reuse terms from well-known vocabularies wherever possible. You should onlydefine new terms yourself if you can not find required terms in existing vocabularies. It is common practice tomix terms from different vocabularies.

Linked Open Vocabularies: http://lov.okfn.org/dataset/lov/Well-known vocabularies: http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/CommonVocabularies

Linked Data: good practice

� Friend-of-a-Friend (FOAF) provides terms for describing people andtheir social network

� SIOC Semantically-Interlinked Online Communities� DOAP Description of a Project� Dublin Core Defines general metadata attributes.� SKOS Simple Knowledge Organization System� SKOS DataZone list of vocabularies available in SKOS schema� Review Vocabulary provides terms for representing reviews.� GoodRelations provides terms for describing products and business

entities.� Music Ontology provides terms for describing artists, albums, tracks,

but also performances, arrangements, etc.� Organization Ontology for describing the structure of organizations.� Linking Open Description of Events (LODE) provides terms for

describing the basic properties of an event and contains a list ofaxioms expressing mapping relationships with other ontologies such asDOLCE, CYC, CIDOC-CRM, Event Ontology, F, and SEM.

� Google, Yahoo and Microsoft have agreed on vocabularies forpublishing structured data on the Web. Their shared 'ontology' ismaintained on schema.org .

� MarineTLO (core) Ontology is a top-level ontology for the marinedomain (also applicable to the terrestrial domain) and MarineTLO(imarine) Ontology is an extension and operational version of theMarineTLO core.

� etc.



� Reusing existing URIs. If you need URI references for geographic places, research areas, generaltopics, artists, books or CDs, you should consider using URIs from existing data sources (for instanceGeonames, DBpedia, Musicbrainz, dbtune, RDF Book Mashup, etc.). The two main benefits of using URIsfrom such data sources are:o The URIs are dereferenceable, meaning that a description of the concept can be retrieved from the Web. o The URIs are already linked to URIs from other data sources.

Well-known Data Sets: http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/DataSetsLinked Data Sets available as RDF Dumps: http://www.w3.org/wiki/DataSetRDFDumpsSparqlEndpoints list: http://www.w3.org/wiki/SparqlEndpoints




Guidance for own term definition:

� Do not define new vocabularies from scratch, but complement existing vocabularies with additional terms(in your own namespace) to represent your data as required.

� Provide for both humans and machines. At this stage in the development of the Web of Data, more peoplewill be coming across your code than machines, even though the Web of Data is meant for machines in the firstinstance. Don't forget to add prose, e.g. rdfs:comment for each term invented. Always provide a label for each termusing the rdfs:label property.

� Make term URIs dereferenceable. It is essential that term URIs are dereferenceable so that clients can look upthe definition of a term. Therefore you should make term URIs dereferenceable following the W3C Best PracticeRecipes for Publishing RDF Vocabularies (http://www.w3.org/TR/swbp-vocab-pub/).

� Make use of other people's terms. Using other people's terms, or providing mappings to them, helps topromote the level of data interchange on the Web of Data, in the same way that hypertext links built the traditionaldocument Web. Common properties for providing such mappings are rdfs:subClassOf or rdfs:subPropertyOf.

� State all important information explicitly. For example, state all ranges and domains explicitly. Remember:humans can often do guesswork, but machines can't. Don't leave important information out!

� Do not create over-constrained, brittle models; leave some flexibility for growth. For instance, ifyou use full-featured OWL to define your vocabulary, you might state things that lead to unintended consequences andinconsistencies when somebody else references your term in a different vocabulary definition. Therefore, unless youknow exactly what you are doing, use RDF-Schema to define vocabularies.




Linked Data Browsers, Mashups and other Client Applications:(http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/SemWebClients)

� Tabulator� OpenLink Data Explorer� DBpedia Mobile� Marbles� Graphity Browser� Quick & Dirty RDF Browser� LODmilla� Etc.

Semantic Web Search Engines:(http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/SemanticWebSearchEngines)

� <sameAs.org>� VisiNav� Falcons� Sindice� Watson� Swoogle� Etc.

Web of Data Tools



Web Intelligence and Service EngineeringInternational Master’s Programhttps://www.jyu.fi/en/studywithus/programmes/wise

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Relevant Courses:� ITKS544 Semantic Web and Ontology Engineering (5 ECTS)

� TIES452 Practical Introduction to Semantic Web Technologies (5 ECT S)

� TIES437 Everything-to-Everything Interfaces (5 ECTS)

� TIES438 Big Data Engineering (5 ECTS)

� …
