tu/e technische universiteit eindhoven assignment 1 if e-mail failed try: –electronically: bscw...

TU/e technische universiteit eindhoven

Assignment 1

• If e-mail failed try:– Electronically: BSCW– Physically: MailBox outside HG7.75– More info:

http://wwwis.win.tue.nl/~houben/wis/

• Deadline extended to today until 23:59


Semantic Web Applications

Kees van der Sluijs


Contents

• Introduction

• Utilization of the Semantic Web

• Selection of Developer Tools

• Selection of End-User Applications

• Examples of Techniques


Introduction


Why Semantic Web?

• You have seen some Whats and Hows– But what can you do with it?


Proposed Benefits

• Information Standardization

• Flexibility

• Semantic Interoperability

• More Collaboration

• Backward and Forward compatibility

• Greater (re-)use of off-the-shelf software


Utilization of the Semantic Web


Semantic Web ‘History’

• XML (1996-2004)

• RDF (1997-2004)

• RDFS (1998-2004)

• OWL (2002-2004)

• SPARQL (2004- ?)

• However Description Logics since 1985


Sorts of Applications• Developer Tools

– In order to create, query, visualize and validate Semantic Web Data

– Semantic Web tools are critically important for its success

• End-user applications– Should give instance benefit to providing semantically

enriched data– Should be natural; hiding SW techniques and data

structures


Semantic Web Tools• Creation Tools

– E.g. Editors, Webforms, etc

– Wrapping of existing data formats

– Natural language extraction, Machine learning, etc

• APIs– For seamless integration of Semantic Web data

structures in different programming languages

• Transformation tools– For exchange of data between applications and users

– E.g. CSS for HTML and XSL(T) for XML


Semantic Web Tools (2)• Visualization tools

– Visualization of complex graph-structure

– Displaying / hiding details

• Reasoning– Combining information on the Semantic Web can

provide new information

– OWL provides Description Logic• Enables First-Order-Logic reasoning with languages like

Prolog


End-user Functionality

• Information Sharing– Information need not be communicated to every

application that uses the same info– Communication, syntactic and semantic

interoperability• Collaborative filtering,

– Recommendation systems, pattern discovery, self-information


End-user Functionality(2)• Data integration

– Create consistent view (e.g. a homogenous presentation) over heterogeneous data-sources

– Adapt data to context

• Personalization

– Propagation of personalization to different applications

– Social Networking


End-user Functionality (3)

• Searching and Retrieval of Data– Not just keywords, query properties and

relationships between concepts– SQL power for the Web!

• Reasoning– Infer new information– Take natural language into account (e.g.

synonyms, homonyms, antonyms, etc)– Decision Support


Selection of Developer Tools


APIs

• HP Jena– (http://jena.sourceforge.net/index.html)

• Sesame– http://www.openrdf.org/

• SWeDE (Eclipse plug-in)– http://owl-eclipse.projects.semwebcentral.org/

• Stanford API (Melnik)– http://www-db.stanford.edu/~melnik/rdf/api.html


Reasoners

• Racer– http://www.racer-systems.com/index.phtml

• FaCT++– http://owl.man.ac.uk/factplusplus/

• Pellet– http://www.mindswap.org/2003/pellet/index.shtml

• Cwm– http://www.w3.org/2000/10/swap/doc/cwm.html


Semantic Web Query

• HP Jena– http://jena.sourceforge.net/index.html– RDQL

• Sesame– http://www.openrdf.org/– SeRQL, RQL, RDQL, (SPARQL plug-in)

• Kowari– http://www.kowari.org/– iTQL


Sesame

“Sesame is an open source RDF database with support for RDF Schema inferencing and querying.”


“An massively scalable, transaction-safe, purpose-built database or the storage and retrieval of metadata.”

Kowari


Editors and Visualizers

• Protégé– http://protege.stanford.edu/

• SWOOP– http://www.mindswap.org/2004/SWOOP/

• KAON– http://kaon.semanticweb.org/

• EROS– wwwis.win.tue.nl/~hera/


Selection of End-User Applications


RDF Site Summary (RSS)

• News publishing mechanism• RSS-aggregators

– Collect different RSS (and XML) ‘feeds’– Enables uniform, personalized view on

heterogeneous data-sources

• Different incompatible versions exist– (Rich Site Summary, Really Simple

Syndication, Atom, etc)


RDF in Mozilla

• Smart Browsing and Related Links– Displaying data from RDF-database by using Stylesheets (XUL)

• Aurora– Integrate all your stuff in a Web browser

• Flash Panel– Collect important information from various sources (e.g. mail, IM,

RSS-feeds, etc)

• Enabling Inference– Using Prolog. Applications: Inter-schema mappings, Reasoning about

user preferences and profiles, Advanced mail-filtering

Ref: http://www.mozilla.org/rdf/doc/


End-User Applications (1)

• RDF Calender– http://www.ilrt.bris.ac.uk/discovery/2001/02/calendar/

• Adobe XMP– http://www.adobe.com/products/xmp/main.html

• Photostuff– http://www.mindswap.org/2003/PhotoStuff/

• SMORE– http://www.mindswap.org/2005/SMORE/

• Piggy Bank– http://simile.mit.edu/piggy-bank/


End-User Applications (2)

• Haystack– http://simile.mit.edu/hayloft/index.html

• FOAF– http://www.foaf-project.org/

• MusicBrainz / AudioScrobbler– http://musicbrainz.org/, http://www.last.fm/

• Hera– http://wwwis.win.tue.nl/~hera


Rdf-Calendar• EventDiscovery

– How do I find and share RDF calendar documents?• CalendarScraping

– Importing data from other formats• TravelTools, PathCross

– Automatically plan routes based on appointments• Planning and negotiation

– Automatically search for possibilities for appointments and meetings

• AnnounceOMatic– Subscribe to particular kind of events, e.g. conferences


Adobe XMP


Adobe Photoshop - XMP


Photostuff


Piggy bank (1)

• FireFox plugin• Brings Semantic Web in Web-browsering• Consists of different steps

– Collect Data– Search and Browse– Pinpoint locations on a map– Tag Information– Combined data– Share Data


Haystack (1)

• Semantic Web browser– “Present Semantic Web data in a integrated

and human presentable way”


Friend Of A Friend


<Person rdf:nodeID="danbri"> <foaf:name>Dan Brickley</foaf:name><foaf:nick>danbri</foaf:nick> <foaf:jabberID>[email protected]</foaf:jabberID><foaf:aimChatID>danbri_2002</foaf:aimChatID> <mbox rdf:resource="mailto:[email protected]"/><uranai:bloodtype xmlns:uranai="http://kota.s12.xrea.com/vocab/uranai" >

A+</uranai:bloodtype> <srw:srw>en</srw:srw>

<homepage rdf:resource="http://rdfweb.org/people/danbri/"/> <foaf:dateOfBirth>1972-01-09</foaf:dateOfBirth> <foaf:img rdf:resource="http://rdfweb.org/people/danbri/2000/01/01/Image1.gif"/> <plan>Save the world and home in time for tea.</plan><knows>

<Person> <name>Dean Jackson</name> <rdfs:seeAlso rdf:resource="http://www.grorg.org/dean/foaf.rdf"/> </Person>

</knows> </Person>


Hera Presentation Generator (HPG)CM (Domain model)

AM (Navigation structure)

Profile (User and platform

characteristics)

CMI (Input data)

PM (Layout and style)

Presentation (Web pages)


HPG - Presentation in Browsers

HTML for PC SMIL


HPG - Presentation on different devices

HTML for PDA WML


Examples of Techniques


Semantic Search

• ‘All papers written by prof. Houben between 2000 and 2004’

Paper

2002

Type

WrittenBy

Name

Houben

Function

Prof.

Publication Year


Current Search Engines

• Query expression power– Only match words– No relations between query elements

• Not all data is in the document (metadata)• Only searches flat text (HTML,pdf,txt, etc)

– Multimedia files only if imbedded in document with surrounding text (works poorly)

– Structure cannot be exploited to increase expression power


Exploiting Semantics

• Not only exact structure queries– You do not know the structure you search on– Relevant source structures are heterogeneous

• Use semantics to get more relevant results– Include similar Classes

• E.g. subClassOf, EquivalentClass, etc

– Search Equivalent constructs– Process Language variations

• E.g. Synonyms, Homonyms, Polysemy


Reasoning

• Reasoning support is important for– checking the consistency of the ontology and the

knowledge– checking for unintended relationships between classes– automatically classifying instances in classes

• Checks like the preceding ones are valuable for – designing large ontologies, where multiple authors are

involved– integrating and sharing ontologies from various sources


Reasoning Example• Terminology (or T-Box)

– Murder Crime : Fraud– Murderer commit.Murder

– Accountantcommit.Crimecommit.Fraud

• Assertions (or A-Box)– Oswald: Criminal

– KennedyAssassination: Murder

– (Oswald, KennedyAssassination): commited


Expansion Rules

Ref: Baader, Sattler 2000


Reasoning Example (2){Murder Crime Fraud, Murderer commit.Murder ,Accountant commit.Crime commit.Fraud}

• Satisfiability (or consistency)– Recursively apply expansion rules– Stop when no more rules applicable or clash occurs

• Clash is an obvious contradiction, e.g., A(x) : A(x)• Concept unsatisfialibity: Murder Fraud = .

• Subsumption– Represents the is-a relation – Check if for all instances i holds that A.i B.i

• Subsumption: Murderer Accountant


Reasoning Example (3){Oswald: Criminal, KennedyAssassination: Murder,

(Oswald, KennedyAssassination): commited}

• Consistency– Check instances against model– E.g. additional assertion {Oswald:Accountant} would

result in contradiction.• Instance Checking

– Check if for all occurences instances i holds that i A– E.g. Oswald Murderer


Information Sharing


User Dependent

Application Dependent

Instance Mapping

Schema A Schema B

maps-from

Instance a Instance b

maps-to

Mapping Module

is-inp

ut-fo

r is-input-for

generates

is-instance-of is-instance-of

Schema Mapping

A B

A+B

generates

tu/e technische universiteit eindhoven assignment 1 if e-mail failed try: –electronically: bscw...

Documents

semantic web slide

xml slide

kowari slide

itql slide

sluijs slide

prolog slide

hera slide

selfinformation slide