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Semantic Web

Semantic Web. Applications Dieter Fensel Katharina Siorpaes. Today’s lecture. Agenda. Motivation Technical solutions and illustriations Applications for data integration (Piggy Bank, Nepomuk ) Applications for knowledge management (SWAML)

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Semantic Web

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  1. Semantic Web Applications Dieter Fensel Katharina Siorpaes

  2. Today’s lecture

  3. 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

  4. Motivation • A wide variety of applications of semantic technologies. • Interesting scenarios: • Data integration • Knowledge management • Indexing • Annotation and enrichment • Discovery (search)

  5. Applications for Data Integration

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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/

  12. 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

  13. 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)

  14. 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

  15. Applications for Knowledge Management

  16. 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

  17. 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

  18. 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

  19. 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/

  20. Applications for Semantic Indexing and Semantic Portals

  21. 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

  22. 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

  23. 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

  24. 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

  25. Watson – An example Search for movie and director Resulting ontologies

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

  27. 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

  28. 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

  29. 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

  30. 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

  31. DBpedia – A query example • SPARQL Query that finds people who were born in Innsbruck before 1900 • Search with regular search mechanism virtually impossible

  32. Applications for description, discovery and selection

  33. 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

  34. 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

  35. 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

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

  37. 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

  38. 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/)

  39. Summary • Application scenarios: • Data integration • Knowledge management • Indexing • Annotation and enrichment • Discovery (search) • PiggyBank • Nepomuk • SWAML • Watson • DBPEDIA • Yahoo! SearchMonkey

  40. 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/

  41. Next Lecture

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