Title: Semantic Web Applications
1Semantic Web Applications
- Prof. Dr. Heiner Stuckenschmidt
- Universität Mannheim
- HWS 2007
2Outline
- The Semantic Web Challenge
- Infomation Portals MultimediaN
- Semantic Browsing COHSE
- Personalized Search MusiDB
- Collaborative Editing Semantic MediaWiki
- Social Network Analysis Flink
3Semantic Web Challenge
- Contest for the most advanced and most useful
application of semantic web technologies - Organized since 2003
- http//challenge.semanticweb.org
- Goal (official) Demostrate the potential of
semantic web technologies for solving real world
problems - Goal (inofficial) Motivate Master and PhD
Students to do lots of programming work -) - Participants about 10 20 each Year
4Waht is a Semantic Web Application?
- Text from the Semantic Web Challenge home page
- A Semantic Web Application has to meet the
following minimal requirements - First, the meaning of data has to play a central
role. - Meaning must be represented using formal
descriptions - Data must be manipulated/processed in interesting
ways to derive useful information and - this semantic information processing has to play
a central role in achieving things that
alternative technologies cannot do as well, or at
all - Second, the information sources used
- should have diverse ownerships (i.e. there is no
control of evolution), - should be heterogeneous (syntactically,
structurally, and semantically), and - should contain real world data, i.e. are more
than toy examples. - Third, it is required that all applications
assume an open world, i.e. assume that the
information is never complete.
5Example 1 MultimeniaN
- Application domain cultural heritage
- Preservation of cultural heritage for the
following generations - Digitalization of collections from important
libraries and museums - Provide access to objects across different
collections and locations - Problems
- Integration of heterogeneous data sources
- Advanced search for objects of a certain style or
period
6Data Sources
- Artchive.com online Art Portal(gt3000 Objekte)
- Rijksmuseum Amsterdam (gt700 Objekte)
- Ethnological Museum Amsterdam (gt 23.000 Objekte)
- Tropical Museum Amsterdam (gt78.000 Objekte)
- Royal Library of the Netherlands(gt1600 Objekte)
7Background Knowledge
- Objects from all collections are linked to
jointly used thesauri and vocabularies - Getty Arts and Architecture Thesaurus (AAT)
- Union List of Artists Names (ULAN)
- Thesaurus of Geographic Names (TGN)
- Ethnological Thesaurus (SVNC)
- General Thesaurus of the English Language
(Wordnet)
8Interface
9Architectur
10Benefits of the Technology
- Unified description of art objects from different
sources and collections - Facetted search Role of query terms is
explicated - Silber as material vs. Silver as part of the
title - Background Information about Objects (style,
period, related artists) - Explicit relations between objects
11Example 2 Semantic Browsing
- Application Searching Information
- Looking for infromation on the web either for
private or for business purposes - Problems
- The usual ones
- Idea
- Extending existing browsers with the ability to
use semantic web contents to support search
12Semantic Web Browsers
- Automatic recognition of objects, persons,
concepts etc. mentioned on a web page on the
basis of an underlying ontology - Support the Annotation of information contents,
e.g. by proposing annotations and keywords - Automatic generation of hyperlinks
- To other objects of the same type
- To pages talking about the same concept
- To background information about objects or
concepts - Example Piggy Bank, Magpie, COHSE
13COHSE
14Benefits of the technology
- Same as above, but
- Applicable to standard web pages, provided that
- A suitable ontology is available
- The terms occurring on the page can be mapped
into this ontology - Limited support for personalization by means of
using special ontologies - Enrichment of exsiting web resources by
supporting semantic annotation of contents
15Example 3 Personalization
- Application
- Proactively suggest relevant contents to the user
- Special Case suggest products the user might
want to buy - Problems
- Lack of information about the users interest
- Interests are often ambiguous (z.B. hard music)
- Classification of objects missing or heterogeneous
16Example MusiDB
- Search for Music
- Music-Ontology Artists, Songs, Albums
http//musicbrainz.org/ - Example Which Album do I have to buy to get this
song? - MusiDB System http//www.cs.vu.nl/rstegers/iwa/
17Interface
18What about unknown music?
Which Style do you prefer ?
19Recommender Systems
Users
2. Feedback
Music
1. Recommendation
3. Improved Recommendation
...
20(No Transcript)
21Benefits of the Technology
- As before
- Unique identifiers for Artists etc.
- Explicit Knowledge about relations
- In addition
- Genre-hierarchy as basis for recommendations
- Classification of artists and songs based on user
feedback
22Example Collaborative Knowledge Creation
- Users jointly annotate Web pages and create
knowledge models (a la Web 2.0) - Prominent examples Social Tagging
- Del.icio.us
- Flikr
- Real Value is added when users jointly create
more complex knowledge structures (i.e. relations)
23Example Semantic Mediawiki
- Follows the Wikipedia paradim
- In addition to text, structured information can
be added using a simple syntax - Classification of Objects
- Properties of and relations between objects
- Demonstration Domain
- Persons, Conferences
24Interface
25Benefits of the Technology
- Large scale acquisition of structured knowledge
- Automatic Generation of Reports and Factsheets
- Semantic Search (e.g. for certain object types)
- Consistent use of type information across
different languages - Enables the use outside the wiki by means of
exporting the knowledge as RDF file, e.g. as a
basis for analyzing relations
26Network Analysis
- Use Information about relations between objects
for futher analyses - Citation analysis
- Social network analysis
- Duplicate Detection
- Semantic Technologies as basis for such analysis
- Unambiguous representation of relations
- Standard syntax, tool support available
27Example Flink
- Aanalysis of relations between people involved in
semantic web technologies - Data extracted automatically from the web
- Authors of the International Semantic Web
Conference - Their publications
- Relations to other authors identified using
Google - Analyses
- Important people in the community
- Relevant topics and their interaction
28Interface
29Benefits of the technology
- As before
- Unique identifiers for objects
- Explicit relations between objects
- In addition
- Derivation of global properties from relations
30Conclusions
- Semantic Web Technologies provide means to
represent strutured information on the web - unique identifiers
- classifications
- relations between objects
- This representation is an important basis for
advanced applications - Search, Personalization, Information aggregation,
Analyses,