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Steps towards a Culture Web. Tumbling Walls & Building Bridges. Interoperability: tearing down the walls between collections. Musea have increasingly nice websites But: most of them are driven by stand-alone collection databases

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Tumbling Walls & Building Bridges

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Tumbling walls building bridges

Steps towards a Culture Web

Tumbling Walls & Building Bridges

Interoperability tearing down the walls between collections

Interoperability: tearing down the walls between collections

  • Musea have increasingly nice websites

  • But: most of them are driven by stand-alone collection databases

  • Data is isolated, both syntactically and semantically

  • If users can do cross-collection search, the individual collections become more valuable!

The web open documents and links

The Web: “open” documents and links



Web link

The semantic web open data and links

The Semantic Web: “open” data and links


“Green Stripe (Mme Matisse)”

Royal Museum of Fine Arts, Copenhagen


“Henri Matisse”

Getty ULAN


Dublin Core



Web link

Principle 1 semantic annotation

Principle 1: semantic annotation

Description of web objects with “concepts” from a shared vocabulary

Principle 2 semantic search

Search for objects which are linked via concepts (semantic link)

Use the type of semantic link to provide meaningful presentation of the search results

Principle 2: semantic search






Principle 3 vocabulary alignment

AAT style/period

Edo (Japanese period)


SVCN period


SVCN is local in-house

ethnology thesaurus

AAT is Getty’s

Art & Architecture Thesaurus

Principle 3: vocabulary alignment


The myth of a unified vocabulary

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

Http e culture multimedian nl


Part of the Dutch national MultimediaN project

CWI, VU, UvA, DEN, ICNAlia Amin, Lora Aroyo Mark van Assem, Victor de Boer Lynda Hardman Michiel Hildebrand, Laura Hollink Marco de Niet, Borys Omelayenko Marie-France van Orsouw Jacco van Ossenbruggen Guus Schreiber, Jos Taekema Annemiek Teesing, Anna Tordai Jan Wielemaker, Bob Wielinga

Artchive.comRijksmuseum Amsterdam Dutch ethnology musea (Amsterdam, Leiden)

National Library (Bibliopolis)

Extra slides

Extra slides

From metadata to semantic metadata

From metadata to semantic metadata

Example textual annotation

Example textual annotation

Resulting semantic annotation rendered as html with rdfa

Resulting semantic annotation (rendered as HTML with RDFa)

Levels of interoperability

Levels of interoperability

  • Syntactic interoperability

    • using data formats that you can share

    • XML family is the preferred option

  • Semantic interoperability

    • How to share meaning / concepts

    • Technology for finding and representing semantic links

Term disambiguation is key issue in semantic search

Term disambiguation is key issue in semantic search

  • Post-query

    • Sort search results based on different meanings of the search term

    • Mimics Google-type search

  • Pre-query

    • Ask user to disambiguate by displaying list of possible meanings

    • Interface is more complex, but more search functionality can be offered

Tumbling walls building bridges

Semantic autocompletion

Faceted pre query

Faceted (pre query)



Tumbling walls building bridges


Tumbling walls building bridges

  • v

Multi lingual labels for concepts

Multi-lingual labels for concepts

Learning alignments

Learning alignments

  • Learning relations between art styles in AAT and artists in ULAN through NLP of art historic texts

    • “Who are Impressionist painters?”



  • Basic Semantic Web technology is ready for deployment

  • Web 2.0 facilities fit well:

    • Involving community experts in annotation

    • Personalization, myArt

  • Social barriers have to be overcome!

    • “open door” policy

    • Involvement of general public => issues of “quality”

Semantic interoperability

Semantic interoperability

  • Large, smart web “mash ups”, combining:

    • Data: images, metadata & encyclopaedic knowledge (gazetteers, thesauri, Wikipedia, …)

    • Visualisations: maps, timelines, social networks, …

  • Data too diverse for a traditional database approach

    • fixed schemas will not work

    • data includes relational data, XML text, images, video, …

  • Need to link different data sources together

    • focus on light weight, heuristic approaches

    • reusing as much as possible (web standards)

  • Need new interfaces and search paradigms

    • need to find relations between pieces of information

    • need to organize (cluster/rank/filter) the many relations we will find

Caveats for museum software

Caveats for museum software

  • Be wary of Flash

    • Accessibility

  • Make sure you can connect others and other can connect to you

    • “Don’t buy software which does not support standard open API’s”

  • Export facilities to common formats (XML, …)

Semantic web myths

Semantic Web Myths *)

  • Sem Web = Artificial Intelligence on the Web

  • Relies on centrally controlled ontologies for “meaning”

    • as opposed to a democratic, bottom-up control of terms

  • One has to manually add metadata to all Web pages, relational databases, XML data, etc to use it

  • It is just ugly XML

  • One has to learn formal logic, knowledge representation, description logic, etc.

  • An academic project, of no interest for industry

*) Adapted from a slide by Frank van Harmelen, panel WWW2006

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