Transcript Slide 1

Semantic Web
Applications
Dieter Fensel
Katharina Siorpaes
©www.sti-innsbruck.at
Copyright 2008 STI INNSBRUCK www.sti-innsbruck.at
Today’s lecture
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Date
Title
1
Introduction
2
Semantic Web Architecture
3
RDF and RDFs
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Web of hypertext (RDFa, Microformats) and Web of data
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Semantic Annotations
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Repositories and SPARQL
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OWL
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RIF
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Web-scale reasoning
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Social Semantic Web
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Ontologies and the Semantic Web
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SWS
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Tools
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Applications
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Exam
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Agenda
• Motivation
• Technical solutions and illustriations
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Applications for data integration (Piggy Bank, Nepomuk )
Applications for knowledge management (SWAML)
Applications for Semantic Indexing and Semantic Portals (Watson)
Applications for meta-data annotation and enrichment and semantic
content management (DBPedia)
– Applications for description, discovery and selection (Search
Monkey)
• Extensions
• Summary
• References
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Motivation
• A wide variety of applications of semantic
technologies.
• Interesting scenarios:
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Data integration
Knowledge management
Indexing
Annotation and enrichment
Discovery (search)
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Applications for Data Integration
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Applications for Data Integration
• One of the main advantages of semantic technology is
the interoperability of the used information
• That implies many different data sources
• Applications for data integration allow the use of cross
source queries and merged view on the different
information
• Example applications:
– Piggy Bank
– NEPOMUK the social Semantic desktop
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Piggy Bank - What is it?
• Firefox Extension
• Transforms browser into
mashup platform
• Allows to search and
exchange the collected
information
• Developed as part of the
Simile Project
• Current version: 3.1
*)
*)
*) Source: http://simile.mit.edu/wiki/Piggy_Bank
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Piggy Bank – How does it work?
• Piggy Bank uses RDF
• If a Web page links to RDF,
information is simply retrieved
• Otherwise, information is
extracted from the raw content
• RDF information is stored
locally
• Information can now be
searched, tagged, browsed,
etc.
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Piggy Bank – Features at a glance
• Collect data (different plugins, so
called Screen Scrapers for
information retrieval available)
• Save data for further use
• Tag data to add additional
information for more efficient use
• Browse and search through
stored information
• Share the collected data by
publishing it onto Semantic Bank
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Piggy Bank – Architecture overview
• Firefox 2.0 as application
plattform
• Chrome additions, e.g. menu
commands, toolbars etc.
• XPCOM components bridging
the chrome part and the Java
part
• Java Backend for managing
the collected information
Firefox 2.0
Chrome
Additions
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XPCOM
Backend
Java Code
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NEPOMUK– What is it?
• Nepomuk, The Social
Semantic Desktop
• Nepomuk is an acronym for
Networked Environment for
Personal Ontology-based
Management of Unified
Knowledge
• It is a set of methods, tools and
data structures to extend the
personal computer into
*)
*) Source: http://nepomuk.semanticdesktop.org/xwiki/bin/view/Main1/
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NEPOMUK - Aspects
• Desktop Aspect – tools for
annotating and linking
information on lokal desktop
• Social Aspect – tools for social
relation building and
knowledge exchange
• Community Uptake – build a
community around the Social
Semantic Desktop in order to
use the full potential
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NEPOMUK – Projects on Top
• SemanticDesktop.org
(developer and user
community on the topics of a
„Social Semantic Desktop“)
• NEPOMUK KDE (creating a
semantic KDE environment)
• NEPOMUK Eclipse (enabling a
semantic P2P Semantic
Eclipse Workbench)
• NEPOMUK Mozilla (annotate
Web data and emails)
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NEPOMUK – Ontologies used (excerpt)
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NAO – NEPOMUK Annotation Ontology for
annotating resources
NIE – NEPOMUK Information Element set of
ontologies for describing information elements
– NFO – NEPOMUK File Ontology for
describing files and other desktop
resources
– NCO - NEPOMUK Conctact Ontology for
describing contact information
– NMO – NEPOMUK Message Ontology for
describing emails and instant messages
PIMO – Personal Information Model Ontology
for describing personal information
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Applications for Knowledge
Management
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Applications for Knowledge Management
• Simply storing or organizing information is not enough to turn
information into knowledge
• Knowledge is applied information
• Unless people are able apply to a task information that knowledge is
useless
• Frequently collective knowledge
• Example application: SWAML
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SWAML – What is it?
• Mailinglist store vast
knowledge capital
• Major drawbacks: hard to
query, unstructured, difficult to
work with
• SWAML generates RDF from
mailing list archives,
consequently
• Developed by CTIC
Foundation and the WESO-RG
at University of Oviedo
• Current version: 0.1.0
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SWAML – How does it work?
• mbox as data source
• SWAML core produces RDF
data ; SIOC ontology used
• Enrichment of stored data with
FOAF using Sindice (Semantic
Web Index) as source of
infromation
• Access and use stored
semantic data via Buxon
browser
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SWAML – The SIOC Ontology
• SIOC is an acronym for
Semantically-Interlinked Online
Communities
• Main objective:
– to structure information of
community based sites
– Link information of
community based sites
• Consists of several classes
and properties to describe
community sites (weblogs,
message boards, etc.)
*)
*) Source: http://rdfs.org/sioc/spec/
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Applications for Semantic
Indexing and Semantic Portals
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Applications for Semantic Indexing and
Semantic Portals
• Web already offers topic-specifigc portals and generic structured
directories like Yahoo! or DMOZ
• With semantic technologies such portals could:
– use deeper categorization and use ontologies
– integrate indexed sources from many locations and communities
– provide different structured views on the underlying information
• Example application: Watson
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Watson – What is it?
• Watson is a gateway for the
semantic web
• Provides efficient access point
to the online ontologies and
semantic data
• Is developed at the Knoledge
Media Institute of the Open
Universit in Milton Keynes, UK
*)
*) Source: http://watson.kmi.open.ac.uk/Overview.html
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Watson – How does it work?
• Watson collects available
semantic content on the
Web
• Analyzes it to exstract
useful metadata and
indexes it
• Implements efficient
query facilities to acess
the data
*)
*) Source: http://watson.kmi.open.ac.uk/Overview.html
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Watson – Features at a Glance
• Attempt to provide high quality
semantic data by ranking
available data
• Efficient exploration of implicit
and explicit relations between
ontologies
• Selecting only relevant
ontology modules by
extraciting it from the whole
ontology
• Different interfaces for
querying and navigation as
well as different levels of
formalization
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Watson – An example
Search for movie and director
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Resulting ontologies
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Applications for meta-data
annotation and enrichment and
semantic content management
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Applications for meta-data annotation and
enrichment and semantic content management
• Applications that focus on adding, generating and managing
meta-data of existing information
• Often collaborative applications like Wikis with semantic
capabilities
• Example applications: SemanticMediaWiki, DBpedia
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DBpedia – What is it?
• Approach to extract structured
information from Wikipedia
• Huge knowledge database
consisting of more than 274
million RDF triples
• Allows advanced queries
against the stored information
• Is maintained by Freie
Universität Berlin and
Universität Leipzig
*)
*) Source: http://wiki.dbpedia.org/About
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Dbpedia – How does it work?
• Wikipedia contains structured
information like infoboxes,
categorizations, etc.
• DBpedia extracts this kinds of
structured information and
transforms it into RDFstatements . This is done by
the Dbpedia Information
Extraction Framework
• Provides a SPARQL-endpoint
to access and query the data
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The DBpedia Ontology
• DBpedia Ontology is used to
extract data from infoboxes
• Consists of more than 170
classes and 940 properties
• Manual mappings from infobox
to the Ontology define finegranular rules how to parse
infobox-values
• Does not cover all Wikipedia
infobox and infobox properties
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DBpedia – A query example
• SPARQL Query that
finds people who were
born in Innsbruck before
1900
• Search with regular
search mechanism
virtually impossible
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Applications for description,
discovery and selection
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Applications for description, discovery and
selection
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Category of applications the are closely related to semantic indexing
and knowledge management
Applications mainly for helping users to locate a resource, product or
service meeting their needs
Example application: SearchMonkey
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SearchMonkey – What is it?
• Search monkey is a framework
for creating small applications
that enhance Yahoo! Search
results
• Additional data, structure,
images and links may be
added to search results
• Yahoo provides meta-data
*)
*) Source: http://developer.yahoo.com/searchmonkey/smguide/index.html
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SearchMonkey – An example application
• IMDB Infobar
• Enhance searches for
imdb.com/name and
imdb.com/title
• Adds information about the
searched movie and links to
the search result
• May be added individually to
enhance once search results
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SearchMonkey – How does it work?
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Applications use two types of data
services: custom ones and ones
provided by Yahoo!
Yahoo! Data services include:
– Indexed Web Data
– Indexed Semantic Web Data
– Cached 3rd party data feeds
Custom data services provide
additional, individual data
SearchMonkey application
processes the provided data and
presents it
*)
*) Source http://developer.yahoo.com/searchmonkey/smguide/data.html
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SearchMonkey – Ontologies used
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Common vocabularies used: Friend of a Friend( foaf), Dublin Core (dc),
VCard(vcard), VCalendar(vcal), etc.
SearchMonkey specific:
– searchmonkey-action.owl: for performing actions as e.g. comparing prices of items
– searchmonkey- commerce.owl: for displaying various information collected about
businesses
– searchmonkey-feed.owl: for displaying information from a feed
– searchmonkey-job.owl: for displaying information found in job descriptions or
recruitment postings
– searchmonkey-media.owl: for displaying information about different media types
– searchmonkey-product.owl: for displaying information about products or
manufacturers
– searchmonkey-resume.owl: for displaying information from a CV
•
SearchMonkey does not support reasoning of OWL data
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Extensions
•
•
More information about tools and applications of semantic technologies
is available at http://semanticweb.org/wiki/Tools
Semantic technologies are applied in case studies in various EU
projects (e.g. http://www.sti-innsbruck.at/research/projects/)
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Summary
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Application scenarios:
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Data integration
Knowledge management
Indexing
Annotation and enrichment
Discovery (search)
PiggyBank
Nepomuk
SWAML
Watson
DBPEDIA
Yahoo! SearchMonkey
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References
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http://www.w3.org/2001/sw/Europe/reports/chosen_demos_rationale_report/hpapplications-selection.html
http://dbpedia.org/About
http://watson.kmi.open.ac.uk/Overview.html
http://semanticweb.org/wiki/Main_Page
http://simile.mit.edu/wiki/Piggy_Bank
http://swaml.berlios.de/
http://developer.berlios.de/projects/swaml/
http://rdfs.org/sioc/spec/
http://watson.kmi.open.ac.uk/Overview.html
http://developer.yahoo.com/searchmonkey/
www.sti-innsbruck.at
Next Lecture
#
Date
Title
1
Introduction
2
Semantic Web Architecture
3
RDF and RDFs
4
Web of hypertext (RDFa, Microformats) and Web of data
5
Semantic Annotations
6
Repositories and SPARQL
7
OWL
8
RIF
9
Web-scale reasoning
10
Social Semantic Web
11
Ontologies and the Semantic Web
12
SWS
13
Tools
14
Applications
15
Exam
www.sti-innsbruck.at