Assignment 1

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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 2359

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Semantic Web Applications

• Kees van der Sluijs

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Contents

• Introduction
• Utilization of the Semantic Web
• Selection of Developer Tools
• Selection of End-User Applications
• Examples of Techniques

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Introduction
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Why Semantic Web?

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

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Proposed Benefits

• Information Standardization
• Flexibility
• Semantic Interoperability
• More Collaboration
• Backward and Forward compatibility
• Greater (re-)use of off-the-shelf software

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Utilization of the Semantic Web
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Semantic Web History

• XML (1996-2004)
• RDF (1997-2004)
• RDFS (1998-2004)
• OWL (2002-2004)
• SPARQL (2004- ?)
• However Description Logics since 1985

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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

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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

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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

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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

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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

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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

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Selection of Developer Tools
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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

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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

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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

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Sesame
Sesame is an open source RDF database with
support for RDF Schema inferencing and querying.
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Kowari
An massively scalable, transaction-safe,
purpose-built database or the storage and
retrieval of metadata.
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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/

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Selection of End-User Applications
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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)

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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/

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End-User Applications (1)

• RDF Calender
• http//www.ilrt.bris.ac.uk/discovery/2001/02/calen
  dar/
• 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/

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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

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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

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Adobe XMP
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Adobe Photoshop - XMP
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Photostuff
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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

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Haystack (1)

• Semantic Web browser
• Present Semantic Web data in a integrated and
  human presentable way

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Friend Of A Friend
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ltPerson rdfnodeID"danbri"gt ltfoafnamegtDan
Brickleylt/foafnamegt ltfoafnickgtdanbrilt/foafnick
gt ltfoafjabberIDgtdanbri_at_gnu.minu.nult/foafjabber
IDgt ltfoafaimChatIDgtdanbri_2002lt/foafaimChatIDgt
ltmbox rdfresource"mailtodanbri_at_w3.org"/gt ltur
anaibloodtype xmlnsuranai"http//kota.s12.xrea.
com/vocab/uranai" gt
Alt/uranaibloodtypegt ltsrwsrwgtenlt/srwsrwgt
lthomepage rdfresource"http//rdfweb.org/people
/danbri/"/gt ltfoafdateOfBirthgt1972-01-09lt/foafd
ateOfBirthgt ltfoafimg rdfresource"http//rdfwe
b.org/people/danbri/2000/01/01/Image1.gif"/gt
ltplangtSave the world and home in time for
tea.lt/plangt ltknowsgt ltPersongt ltnamegtDean
Jacksonlt/namegt ltrdfsseeAlso
rdfresource"http//www.grorg.org/dean/foaf.rdf"/
gt lt/Persongt lt/knowsgt lt/Persongt
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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)
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HPG - Presentation in Browsers
HTML for PC
SMIL
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HPG - Presentation on different devices
HTML for PDA
WML
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Examples of Techniques
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Semantic Search

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

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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

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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

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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

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Reasoning Example

• Terminology (or T-Box)
• Murder ? Crime ? Fraud?
• Murderer ? ??commit.Murder
• Accountant??commit.Crime???commit.Fraud
• Assertions (or A-Box)
• Oswald Criminal
• KennedyAssassination Murder
• (Oswald, KennedyAssassination) commited

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Expansion Rules
Ref Baader, Sattler 2000
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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

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Reasoning Example (3)

• Oswald Criminal, KennedyAssassination Murder,
  (Oswald, KennedyAssassination) commited
• Consistency
• Check instances against model
• E.g. additional assertion OswaldAccountant
  would result in contradiction.
• Instance Checking
• Check if for all occurences instances i holds
  that i ? A
• E.g. Oswald ? Murderer

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Information Sharing
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