SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Enhancing Museum Collections Management Through a Semantic Web Approach

novia avatar
novia
36 diapositives · 495 Views · Aug 22, 2014

This presentation explores the integration of semantic web technologies into museum collections management. It highlights the constraints of current management methods and proposes an effective semantic web archive system to enhance cultural heritage preservation. By improving data interoperability and creating a knowledge management network among museums, the project aims to address the isolation of museum collections. Key technologies include XML, RDF, and ontologies, with a focus on using established standards like CIDOC CRM and Dublin Core for effective data representation and management.

#enhancing museum collections management through a semantic web approach #enhancing museum collections management through a semantic web approach ppt #powerpoint #presentation
Enhancing Museum Collections Management Through a Semantic Web Approach - slide 1
01 / 36Enhancing Museum Collections Management Through a Semantic Web Approach
1 / 17

Télécharger la présentation

Enhancing Museum Collections Management Through a Semantic Web Approach
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  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.