1. www.sti-innsbruck.at © Copyright 2008 STI INNSBRUCK www.sti-innsbruck.at Semantic Web Applications Lecture XIV Dieter Fensel

2. www.sti-innsbruck.at Today’s lecture #DateTitle 1Introduction 2Semantic Web Architecture 3RDF and RDFs 4Web of hypertext (RDFa, Microformats) and Web of data 5Semantic Annotations 6Repositories and SPARQL 7OWL 8RIF 9Web-scale reasoning 10Social Semantic Web 11Ontologies and the Semantic Web 12SWS 13Tools 14Applications 15Exam

3. www.sti-innsbruck.at Today‘s lecture –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)

4. www.sti-innsbruck.at Applications for Data Integration

5. www.sti-innsbruck.at 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 5

6. www.sti-innsbruck.at 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 6 *) *) Source: http://simile.mit.edu/wiki/Piggy_Bank *)

7. www.sti-innsbruck.at 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. 7

8. www.sti-innsbruck.at 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 8

9. www.sti-innsbruck.at 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 XPCOM Backend Java Code 9

10. www.sti-innsbruck.at 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 10 *) Source: http://nepomuk.semanticdesktop.org/xwiki/bin/view/Main1/ *)

11. www.sti-innsbruck.at 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 11

12. www.sti-innsbruck.at 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) 12

13. www.sti-innsbruck.at 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 13

14. www.sti-innsbruck.at 14 Applications for Knowledge Management

15. www.sti-innsbruck.at 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 15

16. www.sti-innsbruck.at 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 16

17. www.sti-innsbruck.at 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 17

18. www.sti-innsbruck.at 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.) 18 *) *) Source: http://rdfs.org/sioc/spec/

19. www.sti-innsbruck.at 19 Applications for Semantic Indexing and Semantic Portals

20. www.sti-innsbruck.at 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 20

21. www.sti-innsbruck.at 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 21 *) *) Source: http://watson.kmi.open.ac.uk/Overview.html

22. www.sti-innsbruck.at 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 22 *) Source: http://watson.kmi.open.ac.uk/Overview.html *)

23. www.sti-innsbruck.at 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 23

24. www.sti-innsbruck.at Watson – An example 24 Search for movie and director Resulting ontologies

25. www.sti-innsbruck.at 25 Applications for meta-data annotation and enrichment and semantic content management

26. www.sti-innsbruck.at 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 26

27. www.sti-innsbruck.at 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 27 *) Source: http://wiki.dbpedia.org/About *)

28. www.sti-innsbruck.at 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 28

29. www.sti-innsbruck.at 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 29

30. www.sti-innsbruck.at DBpedia – A query example SPARQL Query that finds people who were born in Innsbruck before 1900 Search with regular search mechanism virtually impossible 30

31. www.sti-innsbruck.at 31 Applications for description, discovery and selection

32. www.sti-innsbruck.at 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 32

33. www.sti-innsbruck.at 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 33 *) Source: http://developer.yahoo.com/searchmonkey/smguide/index.html *)

34. www.sti-innsbruck.at 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 34

35. www.sti-innsbruck.at 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 35 *) *) Source http://developer.yahoo.com/searchmonkey/smguide/data.html

36. www.sti-innsbruck.at 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 36

37. www.sti-innsbruck.at References http://www.w3.org/2001/sw/Europe/reports/chosen_demos_rationale_report/hp- applications-selection.htmlhttp://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/

38. www.sti-innsbruck.at Next Lecture #DateTitle 1Introduction 2Semantic Web Architecture 3RDF and RDFs 4Web of hypertext (RDFa, Microformats) and Web of data 5Semantic Annotations 6Repositories and SPARQL 7OWL 8RIF 9Web-scale reasoning 10Social Semantic Web 11Ontologies and the Semantic Web 12SWS 13Tools 14Applications 15Exam

39. www.sti-innsbruck.at © Copyright 2008 STI INNSBRUCK www.sti-innsbruck.at Questions? Lecture XIV Dieter Fensel