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Tearing down walls and Building bridges Principles and pragmatics of a Semantic Culture Web Tearing down walls and Building bridges Principles and pragmatics of a Semantic Culture Web

Overview • Virtual collections and Semantic Web • Semantic collection-search demonstrator – For cultural Overview • Virtual collections and Semantic Web • Semantic collection-search demonstrator – For cultural heritage objects • Metadata & vocabulary representation and enrichment • Principles for knowledge engineering on the Web

Acknowledgements • Part of large Dutch knowledge-economy project Multimedia. N • Partners: VU, CWI, Acknowledgements • Part of large Dutch knowledge-economy project Multimedia. N • Partners: VU, CWI, Uv. A, DEN, ICN • People: Alia 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. com, Rijksmuseum Amsterdam, Dutch ethnology musea (Amsterdam, Leiden), National Library (Bibliopolis)

Hypothesis • Semantic Web technology is in particular useful in knowledge-rich domains or formulated Hypothesis • Semantic Web technology is in particular useful in knowledge-rich domains or formulated differently • If we cannot show added value in knowledgerich domains, then it may have no value at all

The Web: resources and links Web link URL The Web: resources and links Web link URL

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

Principle 1: semantic annotation • Description of web objects with “concepts” from a shared 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 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 Query “Paris” Paris Part. Of Montmartre

The myth of a unified vocabulary • In large virtual collections there always multiple The myth of a unified vocabulary • In large virtual collections there 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

Principle 3: vocabulary alignment “Tokugawa” AAT style/period Edo (Japanese period) Tokugawa AAT is Getty’s Principle 3: vocabulary alignment “Tokugawa” AAT style/period Edo (Japanese period) Tokugawa AAT is Getty’s Art & Architecture Thesaurus SVCN period Edo SVCN is local in-house ethnology thesaurus

A link between two thesauri A link between two thesauri

Levels of interoperability • Syntactic interoperability – using data formats that you can share 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

Distributed vs. centralized collection data • Minimal requirement: collection object has image URI • Distributed vs. centralized collection data • Minimal requirement: collection object has image URI • Preference for external metadata, accessed through protocol such as OAI • In practice, external metadata access is still cumbersome

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

Search strategies • Basic search: keyword-oriented • Advanced search: – Tweaking default search parameters Search strategies • Basic search: keyword-oriented • Advanced search: – Tweaking default search parameters – Time-related queries • Faceted search • Relation search – How are two URIs related?

Keyword search with semantic clustering 1. Btree of literals plus Porter stem and metaphone Keyword search with semantic clustering 1. Btree of literals plus Porter stem and metaphone index 2. Find resources with matching labels • Default resources are “Work”s 3. Find related resources by one-way graph traversal • • owl: inverse. Of is used Threshold used for constraining search 4. Cluster results (group instances)

Search: Word. Net patterns that increase recall without sacrificing precisions Search: Word. Net patterns that increase recall without sacrificing precisions

Term disambiguation is key issue in semantic search • Post-query – Sort search results 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

Faceted search • Use Dublin Core scheme to formulate complex queries • Navigate through Faceted search • Use Dublin Core scheme to formulate complex queries • Navigate through relevant metadata

Faceted search Faceted search

What do you need to do to make your collection part of a Semantic What do you need to do to make your collection part of a Semantic Culture Web? Four activities

From metadata to semantic metadata 1. Make vocabulary interoperable 4. Align vocabulary 2. Align From metadata to semantic metadata 1. Make vocabulary interoperable 4. Align vocabulary 2. Align metadata schema 3. Enrich metadata

Activity 1: syntactic vocabulary interoperability • Making vocabularies available in the Web standard RDF Activity 1: syntactic vocabulary interoperability • Making vocabularies available in the Web standard RDF • Many organizations already do this • W 3 C provides the SKOS template to make this almost straightforward • Effort required: at most a few days

Multi-lingual labels for concepts 33 Multi-lingual labels for concepts 33

Semantic relation: broader and narrower • No subclass semantics assumed! 34 Semantic relation: broader and narrower • No subclass semantics assumed! 34

Activity 2: aligning the metadata schema • Specify your collection metadata scheme as a Activity 2: aligning the metadata schema • Specify your collection metadata scheme as a specialization of Dublin Core • With RDF/OWL this is easy/trivial! • Cf. DC Application Profiles

Aligning VRA with Dublin Core • VRA is specialization of Dublin Core for visual Aligning VRA with Dublin Core • VRA is specialization of Dublin Core for visual resources • VRA properties “material. medium” and “material. support” are specializations of Dublin Core property “format” vra: material. medium rdfs: sub. Property. Of dc: fotmat. vra: material. medium rdfs: sub. Property. Of dc: format.

Activity 3: enriching the metadata • Extracting additional concepts from an annotation – Matching Activity 3: enriching the metadata • Extracting additional concepts from an annotation – Matching the string “Paris” to a vocabulary term • Information-extraction techniques exists (and continue to be developed) • Effort required can be up to a few weeks – The more concepts, the better, but no need to be perfect!

Example textual annotation Example textual annotation

Resulting semantic annotation (rendered as HTML with RDFa) Resulting semantic annotation (rendered as HTML with RDFa)

RDFa: embedding RDF in (X)HTML Regular HTML with RDFa Resulting RDF statements 41 RDFa: embedding RDF in (X)HTML Regular HTML with RDFa Resulting RDF statements 41

Activity 4: aligning the vocabulary • Find semantic links between vocabulary links – Derain Activity 4: aligning the vocabulary • Find semantic links between vocabulary links – Derain (ULAN) related-to Fauve (AAT)) • Automatic techniques exists, but performance varies • Often combination of automatic and manual alignment • Effort strongly dependent on vocabularies – But “a little semantic goes a long way” (Hendler)

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

Extracting additional knowledge from scope notes Extracting additional knowledge from scope notes

Principles for knowledge engineering on the Web Principles for knowledge engineering on the Web

Principle 1: Be modest! • Ontology engineers should refrain from developing their own idiosyncratic Principle 1: Be modest! • Ontology engineers should refrain from developing their own idiosyncratic ontologies • Instead, they should make the available rich vocabularies, thesauri and databases available in web format • Initially, only add the originally intended semantics

Principle 2: Think large! Doug Lenat Principle 2: Think large! Doug Lenat "Once you have a truly massive amount of information integrated as knowledge, then the human-software system will be superhuman, in the same sense that mankind with writing is superhuman compared to mankind before writing. "

Principle 3: Develop and use patterns! • Don’t try to be (too) creative • Principle 3: Develop and use patterns! • Don’t try to be (too) creative • Ontology engineering should not be an art but a discipline • Patterns play a key role in methodology for ontology engineering • See for example patterns developed by the W 3 C Semantic Web Best Practices group http: //www. w 3. org/2001/sw/Best. Practices/ • SKOS can also be considered a pattern

Principle 4: Don’t recreate, but enrich and align • Techniques: – Learning ontology relations/mappings Principle 4: Don’t recreate, but enrich and align • Techniques: – Learning ontology relations/mappings – Semantic analysis, e. g. Onto. Clean – Processing of scope notes in thesauri

Principle 5: Beware of ontological over-commitment! Principle 5: Beware of ontological over-commitment!

Principle 6: Specifying a data model in OWL does ot make it an ontology! Principle 6: Specifying a data model in OWL does ot make it an ontology! • Papers about your own idiosyncratic “university ontology” should be rejected at SW conferences • The qality of an ontology does not depend on the number of OWL constrcts sed

Principle 7: Required level of formal semantics depends on the domain! • In our Principle 7: Required level of formal semantics depends on the domain! • In our semantic search we use three OWL constructs: – owl: same. As, owl: Transitive. Property, owl: Symmetric. Property • But cultural heritage has is very different from medicine and bioinformatics – Don’t over-generalize on requirements for e. g. OWL

Perspectives • Basic Semantic Web technology is ready for deployment • Research themes: – Perspectives • Basic Semantic Web technology is ready for deployment • Research themes: – Scalability, vocabulary alignment, metadata extraction • Web 2. 0 facilities fit well: – Involving community experts in annotation – Personalization • Social barriers have to be overcome!