Slides Lecture 11

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d ti f k. l d domain that can be used as a skeletal foundation for a knowledge base ..... eg a ed da ase , c e seco d da ase as ...... “Edward ScissorHands” rdf:type.
INF5120 ”Modellbasert Systemutvikling” ”Modelbased System development”

Lecture 11: 19.04.2010 MDI I: Semantic Web with Ontologies and Model Driven Interoperability

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INF5120 - Lecture plan - 2010 „

1: 25/1: Introduction to MBSU, MDA, OO and Service/SOA modeling, Overall EA, 4 parts: MDE/SSS/MS/MDI (AJB)

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Part I: MDE – Model Driven Engineering 2: 1/2: MDE I: Metamodeling. DSL and UML profiles, MDA technologies (XMI, Eclipse, EMF/GMF) (AJB/BRE)

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Part II: SSS – Service Science and Service/SOA technologies 3: 8/2: SSS I: Service science (top down) - Service and SOA Technologies (bottom up) (AJB)

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Part I continued: MDE – Model Driven Engineering 4: 15/2: MDE II: Model transformations with MOFScript, ATL and other technologies (GO/JO) 5 :22/2: MDE III: Code generation with MOFScript, ATL and other technologies (GO/JO)

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Part III: MOS – Modeling of Services - with SoaML 6: 1/3: MOS I: Business Process Modeling (CIM) - with BPMN 2.0, and BMM, EA with UPDM (AJB) 7: 8/3: MOS II: Soaml, UML2 and SysML, Modelio SOA and Scope, –Collaboration and Component models (AJB) 8: 15/3: MOS III: SoaML (PIM) and Requirements modeling , CIM->PIM and SoaML (AJB) 9: 22/3: MOS IV: Method Engineering and SPEM / EPF - for Service systems (BRE)

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EASTER Part IV – Model Driven Interoperability 10: 12/4: MS V: SOA and Service Design, MDA and ADM - Intro to MDI (AJB ) 11: 19/4: MDI I: Semantic Web with Ontologies and Model Driven Interoperability (TIR) 12: 26/4: MDI II: Semantic Services and Model Driven Interoperability (TIR) 13: 3/5: MDE IV: Evolution and industrial practice of modelbased technologies (AJB++)

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14: 10/5: Course summary and preparation for Exam 31/5 (AJB)

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Exam: May 31st, 31st 2010 (Monday), (Monday) 0900 0900-1200 1200 (3 hours)

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Agenda g „ Model Driven Interoperability „ Ontologies „ Intro to „ Ontology engineering

„ Semantic Web „ Intro „ RDF, RDFS, and SPARQL „ Tool support for RDF/RDFS

„ Conclusions

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Agenda g „ Model Driven Interoperability „ Ontologies „ Intro to „ Ontology engineering

„ Semantic Web „ Intro „ RDF, RDFS, and SPARQL „ Tool support for RDF/RDFS

„ Conclusions

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European Interoperability Framework for European Public Services (EIF) Version 2.0 http://www.bigwobber.nl/wp-content/uploads/2009/11/European-Interoperability-Framework-for-European-Public-Services-draft.pdf

„ Interoperability, within the context of European Public Services delivery, is the ability of disparate and diverse organisations to interact towards mutually beneficial and agreed common goals, involving the sharing of information and knowledge between the organisations, through the business processes they support, byy means of the exchange of data between their respective ICT systems „ An interoperability framework is an agreed approach to interoperability for organisations that wish to work together towards the joint delivery of public services. i Withi its Within it scope off applicability, li bilit it specifies ifi a sett off common elements: vocabulary, concepts, principles, policies, guidelines, recommendations, and practices

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The Needs and the Benefits of Interoperability (EIF) „ Interoperability addresses the need for: „ cooperation between public administrations aiming at the establishment of public

services; „ exchanging information between public administrations to fulfil legal requirements

or p political commitments; „ sharing and reusing information among public administrations to increase administrative efficiency and reduce administrative burden on citizens and businesses;

„ leading to: „ improving public service delivery to citizens and business by facilitating the one-

stop shop delivery of public services; „ reducing g costs for p public administrations,, businesses and citizens through g efficient and effective delivery of public services.

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Interoperability p y Levels ((EFI))

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Compatibility Levels (IEC TC 65/290/DC)

• Incompatibility: inability of two or more devices to work together in the same application • Coexistence: ability to of two or more devices operate independently of one another in the same communication network • Interworkability: ability of two or more devices to support transfer ofdevice parameters

• Interoperability: ability of two or more devices to work together in one or more applications • Interchangeability: ability of two or more devices to replace each other inworking together in one or more application

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Reference model for conceptual integration with ontologies Enterprise System A

Enterprise System B

(MDD Abstraction)

(MDD Abstraction)

Computational Independent Model (CIM)

Semantic Annotation

MT

Model-Driven Development (MDD) & Architecture-Driven Modernisation (ADM)

Interoperability Patterns

Platform Independent Model (PIM)

Model-Driven Development (MDD)

MT

Semantic Annotation

e ic rv cts Se pe As

Architecture-Driven Modernisation (ADM)

Horisontal

Platform Specific Model (PSM)

Pr As oce pe ss ct s

Integration

Ontologies

S Semantic ti Annotation

Reference Ontology

Semantic Annotation

In fo As rm p e a ti ct on s

MT

n- nal No tio ts nc pec u F As

Model-Driven Development (MDD) & Architecture-Driven Modernisation (ADM)

Platform Independent Model (PIM)

Model-Driven Development (MDD)

MT

Semantic Annotation

Execution Platform A

Architecture-Driven Modernisation (ADM)

Platform Specific Model (PSM)

Execution Platform B

Computational System A MT Model Transformation

Computational Independent Model (CIM)

Ontologies

MI Integration Vertical

Semantic S ti Annotation

Computational System B MI Model Interoperability

MT Model Transformation

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Agenda g „ Model Driven Interoperability „ Ontologies „ Intro to „ Ontology engineering

„ Semantic Web „ Intro „ RDF, RDFS, and SPARQL „ Tool support for RDF/RDFS

„ Conclusions

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Ontologies g „ Ontologies provide a shared understanding of a domain „ They provide background knowledge to systems to automatize certain tasks „ By the process of annotation, knowledge can be linked to ontologies „ Example: “Angelina Jolie” (Text) linked to concept Actress „ In our ontology we also know that an actress always is female and a

person

„ Ontologies allow the creation of annotations Æ machine-readable and machine-understandable content „ If machines can understand content, they can also perform more meaningful and intelligent queries „ Distinction of Jaguar the animal and the car „ Combination of information that is distributed on the Web

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Definitions „ An ontology defines the basic terms and relations comprising the vocabulary b l off a topic t i area, as wellll as th the rules l ffor combining bi i tterms and relations to define extensions to the vocabulary „ An ontology is a hierarchically structured set of terms for describing a d domain i th thatt can b be used d as a skeletal k l t l ffoundation d ti ffor a kknowledge l d base „ An ontology provides the means for describing explicitly the conceptualization t li ti b behind hi d th the kknowledge l d represented t d iin a kknowledge l d base „ An ontology is a formal, explicit specification of a shared conceptualization t li ti

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Examples p

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Features of an ontology gy „ Modelled knowledge about a specific domain „ Defines „ A common vocabulary „ The meaning g of terms „ How terms are interrelated

„ Consists of „ Conceptualization and implementation

„ Contains „ Ontological primitives

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Classifications of ontologies

Catalog/ID

Thessauri “narrower term” relation Terms/ glossary

Informal is-a

Formal is-a

Frames (properties)

Formal instance

General Logical constraints

Value Restrs.

Disjointness, Inverse, part-Of ...

Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. p Knowledge g Systems y Laboratory. y Stanford University. y KSL-01-02. 2001.

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Languages g g for building g ontologies g „ Ontologies can be built using various languages with various d degrees off fformality lit „ „ „ „ „ „ „

Natural language UML ER OWL/RDFS WSML FOL ...

„ The formalism and the language limit the kind of knowledge that can be represented „ A domain model is not necessarily a formal ontology only because it is written in OWL

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Applications pp of ontologies g „ Knowledge representation „ Ontology models domain knowledge

„ Semantic annotation „ Ontology is used as a vocabulary vocabulary, classification or indexing

schema for a collection of items

„ Semantic search „ Ontology is used as a query vocabulary or for query rewriting

purposes

„ Configuration g „ Ontology defines correct configuration templates

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Principles p for the design g of ontologies g „ Clarity „ To T communicate i t the th intended i t d d meaning i off d defined fi d tterms

„ Coherence „ To sanction inferences that are consistent with definitions

„ Extendibility E t dibilit „ To anticipate the use of the shared vocabulary

„ Minimal Encoding Bias „ To be independent of the symbolic level

„ Minimal Ontological Commitments „ To make as few claims as possible about the world

Gruber, T.; Towards Principles for the Design of Ontologies. KSL-93-04. Knowledge Systems Laboratory. Stanford University. 1993

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Ontology gy engineering g g „ The set of activities that concern the ontology development process, tthe e ontology o to ogy life e cycle, cyc e, a and d tthe e methodologies, et odo og es, too tools sa and d languages a guages for o building ontologies „ Ontology life cycle

Project management: controlling, planing, quality assurance etc.

E Evaluati ion

Doocumentaation

Knowlledge acquisition n

Domain analysis motivating scenarios, competency questions, existing solutions

Conceptualization conceptualization of the model, integration and extension of existing solutions

Implementation implementation of the formal model in a representation language

Usage/Maintenance adaptation of the ontology according to new requirements Telecom and Informatics

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Ontology engineering activities Management

Development oriented

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Support

Methodologies g for building g ontologies g „ Validated guidelines on how the ontology building process should be structured „ Well-organized process instead of „ontology development driven by inspiration and intuition only“ only „ Methodologies do not support all of the aforementioned activities „ They implicitly assume a particular development paradigm for the ontology engineering process „ Some S off them th also l provide id supporting ti methods th d and d ttools l „ There are many different methodologies for building ontologies Telecom and Informatics

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How to build an ontology? Natalya F. Noy and Deborah L. McGuinness. ``Ontology Development 101: A Guide to Creating Your First Ontology''. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.

„ Step 1: Determine the domain and scope of the ontology „ What is the domain that the ontology will cover? „ For what we are going to use the ontology? „ For what types of questions the information in the ontology should

provide answers? ? „ Who will use and maintain the ontology?

„ Competency p y Questions „ A set of queries which place demands on the underlying ontology „ Ontology must be able to represent the questions using its

terminology and the answers based on the axioms „ Ideally, in a staged manner, where consequent questions require the input from the preceeding ones „ A rationale for each competency question should be given

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How to build an ontology? (cont’) Natalya F. Noy and Deborah L. McGuinness. ``Ontology Development 101: A Guide to Creating Your First Ontology''. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.

„ Step 2: Consider reusing existing ontologies „ Reuse ensures interoperability and reduces costs „ Ontology libraries and tools for customization are required for this step „ Sub-steps „ „ „ „

Discover potential reuse candidates Evaluate their usability Customize ontologies to be reused Integrate and merge to the target ontology

„ Step 3: Enumerate important terms in the ontology „ What are the terms we would like to talk about? „ What properties do those terms have? „ What would we like to say about those terms?

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How to build an ontology? (cont’) Natalya F. Noy and Deborah L. McGuinness. ``Ontology Development 101: A Guide to Creating Your First Ontology''. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.

„ Step 4: Define classes and class hierarchy „ From the list created in Step 3, select the terms that describe objects

having independent existence rather than terms that describe these objects „ These terms will be classes in the ontology „ Organize the classes into a hierarchical taxonomy by asking if by being an instance of one class, the object will necessarily (i.e., by definition) be an instance of some other class „ If a class l A is i a superclass l off class l B, B then th every iinstance t off B is i also l an instance of A „ Classes as unary predicates—questions that have one argument. For example, “Is this object a wine?” „ Later: binary predicates (or slots)—questions that have two arguments. For example, “Is the flavor of this object strong?” “What is the flavor of this object?”

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How to build an ontology? (cont’) Natalya F. Noy and Deborah L. McGuinness. ``Ontology Development 101: A Guide to Creating Your First Ontology''. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.

„ Step 5: Define attributes and relationships „ Step 4 selected classes from the list of terms we created in Step 3

Most of the remaining terms are likely to be properties of these classes „ For each property in the list, we must determine which class it describes „ Types of properties „ “intrinsic” “i t i i ” properties ti „ “extrinsic” properties „ parts, if the object is structured (physical or abstract) „ relationships l ti hi tto other th individuals i di id l „ Properties are inherited and should be attached to the most general class in the hierarchy „

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Agenda g „ Model Driven Interoperability „ Ontologies „ Intro to „ Ontology engineering

„ Semantic Web „ Intro „ RDF, RDFS, and SPARQL „ Tool support for RDF/RDFS

„ Conclusions

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World Wide Web ("WWW" or simply the "Web") „ “A system of interlinked, hypertext documents that runs over the I t Internet. t With a W Web bb browser, a user views i W Web b pages th thatt may contain text, images, and other multimedia and navigates between them using hyperlinks” http://en.wikipedia.org/wiki/World_Wide_Web „ The Th success off WWW is i based b d on three th simple i l principles: i i l 1. A simple and uniform addressing schema to indentify information chunks

i.e. Uniform Resource Identifiers (URIs) 2 A simple and uniform 2. niform representation formalism to str structure ct re information chunks allowing browsers to render them i.e. Hyper Text Markup Language (HTML) 3. A simple and uniform protocol to access information chunks i.e. Hyper Text Transfer Protocol (HTTP)

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Semantic Web (SW) ( ) „ “An extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” (T. Berners-Lee, J. Hendler, O. Lassila, “The Semantic Web”, Scientific American, May 2001) „ Semantic Web is about: „ Web Data Annotation „ connecting (syntactic) Web objects, like text chunks, images, … to their semantic notion (e.g., this image is about Oslo, Arne J. Berre is a professor) „ Data Linking on the Web (Web of Data) „ g global networking g of knowledge g through g URI,, RDF,, and SPARQL (e.g., connecting my calendar with my rss feeds, my pictures, ...) „ Data Integration over the Web „ seamless integration g of data based on different conceptual p models (e.g., integrating data coming from my two favorite book sellers) Telecom and Informatics

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Linked Data htt //li k dd t http://linkeddata.org/ / „ A term used to describe a recommended best practice for exposing, sharing, h i and d connecting ti pieces i off d data, t iinformation, f ti and d kknowledge l d on the Semantic Web using URIs and RDF

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Data linking g on the Web principles p p „ Use URIs as names for things „ anything, not just documents „ you are not your homepage „ information resources and non-information non information resources

„ Use HTTP URIs „ globally unique names, distributed ownership „ allows people to look up those names

„ Provide useful information in RDF „ when someone looks up a URI

„ Include RDF links to other URIs „ to enable discovery of related information

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Data integration g over the Web „ Data integration involves combining data residing in different sources and providing user with a unified view of these data „ Data integration over the Web can be implemented as follows: 1. 2. 3.

Export the data sets to be integrated as RDF graphs Merge identical resources (i.e. resources having the same URI) from different data sets Start making queries on the integrated data, queries that were not possible on the individual data sets.

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Data integration g over the Web 1.

Export first data set as RDF graph For example the following RDF graph contains information about book “The Glass Palace” by Amitav Ghosh

http://www.w3.org/People/Ivan/CorePresentations/SWTutorial/Slides.pdf Telecom and Informatics

Data integration g over the Web 1.

Export second data set as RDF graph Information about the same book but in French this time is modeled in RDF graph below

http://www.w3.org/People/Ivan/CorePresentations/SWTutorial/Slides.pdf Telecom and Informatics

Data integration over the Web 2.

Merge identical resources (i.e. resources having the same URI) from different data sets

Same URI = Same resource

http://www.w3.org/People/Ivan/CorePresentations/SWTutorial/Slides.pdf Telecom and Informatics

Data integration over the Web Merge identical resources (i.e. resources having the same URI) from different data sets

2.

http://www.w3.org/People/Ivan/CorePresentations/SWTutorial/Slides.pdf Telecom and Informatics

Data integration over the Web 3.

Start making queries on the integrated data

„ A user of the second dataset may ask queries like: “give

me the title of the original book” „ This Thi information i f ti is i nott in i the th second d dataset d t t „ This information can be however retrieved from the integrated eg a ed da dataset, ase , in which c the e seco second d da dataset ase was as connected with the the first dataset

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Semantic Web Architecture

Adapted from http://en.wikipedia.org/wiki/Semantic_Web_Stack Telecom and Informatics

UNICODE,, URI and XML „ UNICODE is the standard international character set „ E.g. used to encode the data in the repository

„U Uniform if R Resource Id Identifiers tifi (URI (URIs)) id identify tif thi things and d concepts „ E.g. used to indentify resources on the Web and in the repository

„ eXtensible Markup Language (XML) is a markup language used d ffor data d t exchange h „ E.g. format that can be wrapped into RDF and imported into the

repository

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RDF,, RDFS and OWL „ Resource Description Framework (RDF) is the HTML of th S the Semantic ti W Web b „ „ „ „ „

Simple way to describe resources on the Web Based on triples V i Various serializations, i li ti iincluding l di one b based d on XML A simple ontology language (RDFS) E.g. language used to store the data in the repository

„ Web Ontology Language (OWL) is a more complex ontology language than RDFS „ Layered language based on DL „ Overcomes some RDF(S) limitations „ E.g. ontology language used to define the schemas used in the

repository

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SPARQL and Rule Languages g g „ SPARQL „ Query language for RDF triples „ A protocol for querying RDF data over the Web „ E.g. E g language used to query the repository from the user interface

„ Rule languages (esp. Rule Interchange Format RIF) „ Extend ontology languages with proprietary axioms „ Based on different types of logics „ Description Logic „ Logic Programming „ E.g. used to enable reasoning over data to infer new knowledge

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RDF Basics „ RDF is a language that enable to describe making statements on resources „ John is father of Bill

„ Statement ((or triple) p ) as a logical g formula P(x, ( , y), where the binary predicate P relates the object x to the object y „ Triple data model: 30)} @prefix

result: x =================

ex: . @prefix vcard: . ex:john vcard:FN "John Smith" ; vcard:N [ vcard:Given "John" ; vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:marriedTo :mary . ex:mary vcard:FN "Mary Smith" ; vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29 .

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SPARQL Queries: Optional p Patterns “Return all people and (optionally) their spouse” PREFIX ex: SELECT ?person, ?spouse WHERE {?person ex:hasAge ?age . OPTIONAL { ?person ex:marriedTo ?spouse } }

result:

@prefix ex: . @prefix vcard: . ex:john vcard:FN "John Smith" ; vcard:N [ vcard:Given "John" ; vcard:Family "Smith" ] ; ex:hasAge 32 ; ex:marriedTo :mary . ex:mary vcard:FN "Mary Smith" ; vcard:N [ vcard:Given "Mary" ; vcard:Family "Smith" ] ; ex:hasAge 29 .

?person ?spouse =============================

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Example – A RDF Graph Modeling Movies movie:Genre movie:Movie rdf:type

rdf:type yp

movie:Romance

rdf:type

movie:Comedy

movie:genre movie:genre movie1 movie:year y “1990”

movie:Role

movie:title movie:hasPart

rdf:type “Edward ScissorHands” r 1

“Edward ScissorHands”

movie:characterName

movie:playedBy

actor1

[http://www.openrdf.org/conferences/eswc2006/Sesa me-tutorial-eswc2006.ppt] Telecom and Informatics

Example p Query y1 „ Select the movies that has a character called “Edward Scissorhands” PREFIX movie: SELECT DISTINCT ?x ?t WHERE { ?x movie:title ?t ; movie:hasPart ?y . ?y movie:characterName ?z . FILTER (?z = “Edward Scissorhands”@en) }

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Example p Query y1 PREFIX movie: SELECT DISTINCT ?x ?t WHERE { ?x movie:title ?t ; movie:hasPart ?y . ?y movie:characterName ?z . FILTER (?z = “Edward Scissorhands”@en) } „ Note the use of “;” This allows to create triples referring to the previous triple pattern (extended version would be ?x movie:hasPart ?y) „ Note as well the use of the language speciation in the filter @en Telecom and Informatics

Example p Query y2 „ Create a graph of actors and relate them to the movies they play in (through a new ‘playsInMovie’ p y relation)) PREFIX movie: PREFIX foaf: CONSTRUCT ?x ?x ?x ? } WHERE {

{ foaf:firstName ?fname. foaf:lastName ?lname. movie:playInMovie o e:p ay o e ? ?m

?m movie:title ?t ; y . movie:hasPart ?y ?y movie:playedBy ?x . ?x foaf:firstName ?fname. ?x foaf:lastName ?lname. }

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Example p Query y3 „ Find all movies which share at least one genre with “Gone with the Wind” PREFIX movie: SELECT DISTINCT ?x2 ?t2 WHERE { ?x1 movie:title ?t1. ?x1 movie:genre ?g1. ?x2 movie:genre ?g2. ?x2 movie:title ?t2. FILTER (?t1 = “Gone with the Wind”@en && ?x1!=?x2 && ?g1=?g2) }

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Tool Support pp for RDF/RDFS „ Ontology editors „ Protégé (http://protege.stanford.edu/)

„ Browser „ /facet (http://slashfacet.semanticweb.org/) ( p g)

„ RFD repositories „ Sesame (http://www.openrdf.org/)

„ APIs „ RDF2Go – Java (http://semanticweb.org/wiki/RDF2Go) „ Jena – Java (http://jena.sourceforge.net/)

„ Validator „ W3C Validator (http://www.w3.org/RDF/Validator/)

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„ Developed by Stanford Medical Informatics „ Has a large user community (approx 30k) „ Support „ Graph view, view consistency check check, web web,

merging

„ No support „ Addition of new basic types „ Limited multi multi-user user support

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/facet „ /facet is a generic browser for heterogeneous semantic web repositories „ Works on any RDFS dataset without ith t any additional dditi l configuration g facets of „ Select and navigate resources of any type „ Make selections based on properties of other other, semantically related, types „ Allows the inclusion of facetspecific display options

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Sesame „ A framework for storage, querying and inferencing of RDF and d RDF S Schema h „ A Java Library for handling RDF „ A Database Server for (remote)access to repositories of RDF data „ Features: „ „ „ „ „ „ „ „

Light-weight Light weight yet powerful Java API SeRQL, SPARQL High scalability (O(10^7) triples on desktop hardware) Various backends (Native Store Store, RDBMS, RDBMS main memory) Reasoning support Transactional support Context support RDF/XML, Turtle, N3, N-Triples Telecom and Informatics

Jena „ A Java framework for building Semantic Web applications „ Initiated by Hewlett Packard (HP) Labs Semantic Web Programme. „ Includes: I l d „ A RDF API „ Reading and writing RDF in RDF/XML, N3 and N-Triples „ An OWL API „ In-memory and persistent storage „ SPARQL query engine

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RDF2Go „ RDF2Go is an abstraction over triple (and quad) stores. It allows developers to program against rdf2go interfaces and choose or change the implementation later easily „ It can be extended: you can create an adapter from any RDF Object Model to RDF2Go object model „ Directly supported implementations: „ Jena 2.4 „ Jena 2.6 „ Sesame 2

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W3C Validator „ RDF Validator „ Parse RDF documents and detects errors w.r.t. the current RDF specification „ Available online service „ Downloadable code „ Based on ARP parser (the one also adopted in Jena)

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Agenda g „ Model Driven Interoperability „ Ontologies „ Intro to „ Ontology engineering

„ Semantic Web „ Intro „ RDF, RDFS, and PARQL „ Tool support for RDF/RDFS

„ Conclusions

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Conclusions „ MDI „ Interoperability is an important and unsolved problem „ Ontologies and Semantic Web technologies have significant potential for

solving interoperability problems

„ Ontologies and Semantic Web aim at automating tasks currently carried out by humans „ Ontologies are about: „ Formal, explicit specification of shared conceptualizations „ Semantic Web is about: „ annotation of data on the Web „ data linking on the Web „ data integration over the Web

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