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COMP 6703 eScience Project Semantic Web for Museums

COMP 6703 eScience Project Semantic Web for Museums. Student : Lei Junran Client/Technical Supervisor : Tom Worthington Academic Supervisor : Peter Strazdins Period : 2006 Semester 1. What is in my presentation. Motivation Objectives Technologies Design Considerations Demonstration

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COMP 6703 eScience Project Semantic Web for Museums

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  1. COMP 6703 eScience ProjectSemantic Web for Museums Student : Lei Junran Client/Technical Supervisor : Tom Worthington Academic Supervisor : Peter Strazdins Period : 2006 Semester 1

  2. Whatis in my presentation • Motivation • Objectives • Technologies • Design Considerations • Demonstration • Conclusion • Future Work

  3. Motivation - Constraints • Constrains of Current Museums Collections Management Methods • Natural features of cultural collections — Rich associations • eg, creator of painting A had other paintings with the same style, which originates from another artist, who drew painting B with the same topic… • Collections are preserved as isolated objects in individual museums

  4. Museums System Example

  5. Museums System Example

  6. Museums System Example

  7. Motivation - Solution • The emerging semantic web technology (W3C Semantic Web) would be proposed to solve the constraints and provide a better way for cultural heritage preservation and management.

  8. Project Objectives • Current Objective - to develop an effective semantic web archive system for museums. • Long Terms - research the promising semantic technology for creating the knowledge management network among museums.

  9. Technologies-What is Semantic Web • Tim Berners-Lee's original web vision involved more than retrieving Hypertext Markup Language (HTML) pages from Web servers. • Make the web a more collaborative medium. • Create a web of data that machines can process

  10. How to make Semantic Web possible? • Make the data smarter. • application-independent, easily discovered, to be described with concrete relationships…

  11. Four Levels of smart data • Text Documents and Database Records • Data just can be used in a single application • XML documents using single vocabulary • Data is now smart enough to move between applications in this museum. • XML documents with mixed vocabularies • Data can be composed from multiple museums or institutes

  12. Four Levels of smart data • Ontologies and rules • data is now smart enough to be described with concrete relationships • new data can be inferred from existing data by following logical rules

  13. Semantic Web Elements and technologies • Metadata • XML • RDF • Ontology

  14. Metadata • Meta-data: meaning of data values; • Example: DATA META DATA John Smith Name 222 Happy Lane Address

  15. XML • XML(Extensible Markup Language) is the syntactic foundation layer of the Semantic Web. • Provides a simple, standard syntax for encoding the meaning of data values, or meta data. • Example: <author> <name> John Smith </name> <address> 222 Happy Lane </address> </author>

  16. XML Metadata benefits • All data are described with a set of predefined vocabulary and syntax. • Enable exchange, interoperability, information integration and application independence.

  17. RDF • The resource described in RDF could be identified by URI. The statement about resource is combined of three elements, or triple. &ns;/location/ Greece Subject &ns;/location/ Europe Object locateAt Predicate

  18. RDF/XML Data Example <swm:location rdf : about = "&ns; / location / Greece"> <swm:locationAt rdf:resource = "&ns; / location / Europe"/> </swm:location>

  19. What are included in Ontology? • Classes: Object, Activity, Location • Relationships: object <locate at> location, company <is a > organization • Properties: Identifier(cardinality 1:1), Type, Creator • Constrains and Rules: If X is true, then Y must also be true. • Functions and Process: • A formal vocabulary (defined terms) for all above

  20. Ontology Languages • Ontology is represented in knowledge representation languages • RDFS (lightweight ontology) • Elements: Class, label, subclassOf, Property, Domain, range, type, subPropertyof… • OWL (Robust ontology) • Elements: RDFS plus someValuesFrom ∃, allValuesFrom ∀, hasValue ∋, minCardinality ≥, cardinality =, intersectionOf, unionOf…

  21. Why Use Ontology • defines the domain vocabulary. • Improve association expression, interoperability • Ontology languages are backed by a rigorous formal logic, which makes the ontology machine-interpretable.

  22. Semantic Levels Summary • Semantic Levels (Redrawn after C. Daconta, et al 2003)

  23. Design Considerations • Use existing ontology • CIDOC CRM • CIDOC: The International Committee for Documentation of the International Council of Museums • CRM: Conceptual Reference Model • A domain ontology for cultural heritage information

  24. Design Considerations • Use existing metadata standard • Dublin Core • A simple yet effective element set for describing a wide range of networked resources. • Simplicity, Commonly understood semantics, Extensibility • Example Elements: Identifier, Description, Format, Date, Creator…

  25. CIDOC CRM • Advantages • Comprehensive and widely accepted • Mappings have been established with major metadata standards • Disadvantages • Includes 81 classes and 132 properties • Vocabulary is too detailed to be used as metadata directly

  26. Solutions • Use subset of CRM • Use Dublin Core Metadata Standard • Redesign the vocabulary of the applied subset when DC can not express the meaning of the subset. • Use DC and subset vocabulary (SWM vocabulary) as metadata

  27. Example of CRM

  28. Example Mixed Use of DC and SWM Vocabulary <swm:activity rdf : about = “ &basens;activity /Textile Lengths 85-1002 Production"> <DC:type>production</DC:type> <DC:identifier>Textile Lengths 85-1002 Production </DC:identifier> <swm:beginDate>1984</swm:beginDate> <swm:endDate>1985</swm:endDate> <swm:locateAt rdf : resource = "&basens; location/Ngkwarlerlaneme camp"/> </ swm:activity>

  29. Elements Relationships

  30. System Architecture

  31. Demonstration

  32. Conclusion • A semantic web prototype system has been developed • A RDF Schema has been designed • The museums collections could be input and transferred to RDF data for preservation

  33. Conclusion • Data is now smart enough to be described with concrete relationships • RDF data output and Batch input increases the interoperability with other semantic systems and provide a convenient transfer way to existing data.

  34. Review the four levels of smart data • Ontologies and rules • data is now smart enough to be described with concrete relationships • new data can be inferred from existing data by following logical rules

  35. Half way of the fourth level • Reasons • Use RDFS (lightweight ontology language); • Use subset of ontology, the relationships is not rich enough. • No enough constrains, rules and associations to infer.

  36. Future Work • Redesign Ontology using robust ontology language (eg. OWL) • Add more constrains and rules for inference • Design system showing more benefits of semantic web technology • Web Services and Taxonomies in Semantic Web.

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