semantic web - sti innsbruck€¦ · 2 12.10,2009 semantic web architecture 3 09.11.2009 rdf and...

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Semantic Web Lecture XIV – 25.01.2010

Applications Dieter Fensel and Katharina Siorpaes

www.sti-innsbruck.at

Today’s lecture

# Date Title

1 12.10,2009 Introduction

2 12.10,2009 Semantic Web Architecture

3 09.11.2009 RDF and RDFs

4 09.11.2009 Web of hypertext (RDFa, Microformats) and Web of data

5 23.11.2009 Semantic Annotations

6 23.11.2009 Repositories and SPARQL

7 07.12.2009 OWL

8 07.12.2009 RIF

9 11.01.2010 Web-scale reasoning

10 11.01.2010 Social Semantic Web

11 11.01.2010 Ontologies and the Semantic Web

12 25.01.2010 SWS

13 25.01.2010 Tools

14 25.01.2010 Applications

15 TBD Exam

2


Agenda

1.  Motivation 2.  Technical solutions and illustriations

1.  Applications for data integration (Piggy Bank, Nepomuk ) 2.  Applications for knowledge management (SWAML) 3.  Applications for Semantic Indexing and Semantic Portals (Watson) 4.  Applications for meta-data annotation and enrichment and

semantic content management (DBPedia) 5.  Applications for description, discovery and selection (Search

Monkey) 3.  Extensions 4.  Summary 5.  References

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MOTIVATION

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Motivation

•  A wide variety of applications of semantic technologies.

•  Interesting scenarios: –  Data integration –  Knowledge management –  Indexing –  Annotation and enrichment –  Discovery (search)

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TECHNICAL SOLUTION AND ILLUSTRATIONS

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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

8


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

9

*)

*) Source: http://simile.mit.edu/wiki/Piggy_Bank

*)


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.

10


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 Firefox2.0

ChromeAddi1ons XPCOM

Backend

JavaCode

12


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

13

*) Source: http://nepomuk.semanticdesktop.org/xwiki/bin/view/Main1/

*)


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)

•  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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www.sti-innsbruck.at 17

Applications for Knowledge Management


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

19


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

20


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.)

21

*)

*) Source: http://rdfs.org/sioc/spec/


Applications for Semantic Indexing and Semantic Portals


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

23


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

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*)

*) Source: http://watson.kmi.open.ac.uk/Overview.html


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

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*) Source: http://watson.kmi.open.ac.uk/Overview.html

*)


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

27

Searchformovieanddirector Resul1ngontologies


Applications for meta-data annotation and enrichment and semantic content management


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

29


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

30

*) Source: http://wiki.dbpedia.org/About

*)


Dbpedia – How does it work?

•  Wikipedia contains structured information like infoboxes, categorizations, etc.

•  DBpedia extracts this kinds of structured information and transforms it into RDF-statements . 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 fine-granular 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


Applications for description, discovery and selection

•  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

35


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

36

*) Source: http://developer.yahoo.com/searchmonkey/smguide/index.html

*)


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?

•  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

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*)

*) Source http://developer.yahoo.com/searchmonkey/smguide/data.html


SearchMonkey – Ontologies used

•  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

39


EXTENSIONS

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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/)

41


SUMMARY

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Summary

•  Application scenarios: –  Data integration –  Knowledge management –  Indexing –  Annotation and enrichment –  Discovery (search)

•  PiggyBank •  Nepomuk •  SWAML •  Watson •  DBPEDIA •  Yahoo! SearchMonkey

43


References

•  http://www.w3.org/2001/sw/Europe/reports/chosen_demos_rationale_report/hp-applications-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/

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Next Lecture

45

# Date Title

1 12.10,2009 Introduction

2 12.10,2009 Semantic Web architecture

3 09.11.2009 RDF and RDFs

4 09.11.2009 Web of hypertext (RDFa, Microformats) and Web of data

5 23.11.2009 Semantic annotations

6 23.11.2009 Repositories and SPARQL

7 07.12.2009 OWL

8 07.12.2009 RIF

9 11.01.2010 Web-scale reasoning

10 11.01.2010 Social Semantic Web

11 11.01.2010 Ontologies and the Semantic Web

12 25.01.2010 SWS

13 25.01.2010 Tools

14 25.01.2010 Applications

15 TBD Exam

www.sti-innsbruck.at 46 46

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