Presentation is loading. Please wait.

Presentation is loading. Please wait.

E-Heritage and the VU Semantic Web group Guus Schreiber Computer Science VU University Amsterdam.

Similar presentations


Presentation on theme: "E-Heritage and the VU Semantic Web group Guus Schreiber Computer Science VU University Amsterdam."— Presentation transcript:

1 E-Heritage and the VU Semantic Web group Guus Schreiber Computer Science VU University Amsterdam

2 Semantic Web @ VU Amsterdam 40 people, two groups: Web & Media (Schreiber), Knowledge Representation & Reasoning (van Harmelen) A few ongoing projects: –europeana.eu: EU culture portal –NL projects on access to cultural heritage: CHIP, Agora –EU NoTube: Web & TV semantic integration –PrestoPrime: user-generated annotations and content for TV archives –EU LarKC: platform for massive distributed incomplete reasoning

3 Characteristics of the Web AAA: Anyone can say Anything about Any Topic The Web is an open world It is impossible to enforce unique names The networl effect: a virtuous circle

4 The Web: resources and links URL Web link

5 The Semantic Web: typed resources and links URL Web link ULAN Henri Matisse Dublin Core creator Painting “Woman with hat SFMOMA

6

7

8

9 The myth of a unified vocabulary In large virtual collections there are always multiple vocabularies –In multiple languages Every vocabulary has its own perspective –You can’t just merge them But you can use vocabularies jointly by defining a limited set of links –“Vocabulary alignment” It is surprising what you can do with just a few links

10 Exempel use of vocabulary alignment “Tokugawa” SVCN period Edo SVCN is local in-house ethnology thesaurus AAT style/period Edo (Japanese period) Tokugawa AAT is Getty’s Art & Architecture Thesaurus

11 Architecture of a Semantic Web application Web pages, databases collections, tables, converters scrapers RDF store RDF files RDF query & inferencing Application

12 Demo using linked data (RPI, Hendler)

13 http://e-culture.multimedian.nl/demo/search

14

15 Search: WordNet patterns that increase recall without sacrificing precisions

16 Enriching the metadata

17 Resulting semantic annotation

18 Learning vocabulary alignments Example: learning relations between art styles in AAT and artists in ULAN through NLP of art historic texts –“Who are Impressionist painters?”

19 Personalized Rijksmuseum Interactive user modeling Recommendations of artworks and art topics

20 Mobile museum tour

21 Video tagging games

22


Download ppt "E-Heritage and the VU Semantic Web group Guus Schreiber Computer Science VU University Amsterdam."

Similar presentations


Ads by Google