semantic web

www.sti-innsbruck.at © Copyright 2008 STI INNSBRUCK www.sti-innsbruck.at

Semantic WebApplications

Dieter Fensel Katharina Siorpaes

www.sti-innsbruck.at

Today’s 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


Agenda

• Motivation

• Technical solutions and illustriations– 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


Motivation

• A wide variety of applications of semantic technologies.

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

4


Applications for Data Integration


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

6


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

7

*)

*) 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.

8


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

10


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

11

*) 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

12


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)

13


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 15

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

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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.)

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*)

*) 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

21


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

22

*)

*) 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

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Search for movie and directorSearch for movie and director Resulting ontologiesResulting ontologies


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

27


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

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*) 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

33


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

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*) 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

37


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

• Application scenarios:

– Data integration– Knowledge management– Indexing– Annotation and enrichment– Discovery (search)

• PiggyBank

• Nepomuk

• SWAML

• Watson

• DBPEDIA

• Yahoo! SearchMonkey

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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/


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

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