COMP 6703 eScience Project Semantic Web for Museums

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COMP 6703 eScience ProjectSemantic Web for
Museums

• Student Lei Junran
• Client/Technical Supervisor Tom Worthington
• Academic Supervisor Peter Strazdins
• Period 2006 Semester 1

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What is in my presentation

• Motivation
• Objectives
• Technologies
• Design Considerations
• Demonstration
• Conclusion
• Future Work

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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

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Museums System Example
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Museums System Example
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Museums System Example
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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.

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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.

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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

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How to make Semantic Web possible?

• Make the data smarter.
• application-independent, easily discovered, to be
  described with concrete relationships

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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

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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

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Semantic Web Elements and technologies

• Metadata
• XML
• RDF
• Ontology

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Metadata

• Meta-data meaning of data values
• Example
• DATA META DATA
• John Smith Name
• 222 Happy Lane Address

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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
• ltauthorgt
• ltnamegt John Smith lt/namegt
• ltaddressgt 222 Happy Lane lt/addressgt
• lt/authorgt

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XML Metadata benefits

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

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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
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RDF/XML Data Example

• ltswmlocation rdf about "ns / location /
  Greece"gt
• ltswmlocationAt rdfresource "ns / location
  / Europe"/gt
• lt/swmlocationgt

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What are included in Ontology?

• Classes Object, Activity, Location
• Relationships object ltlocate atgt location,
  company ltis a gt organization
• Properties Identifier(cardinality 11), 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

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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

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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.

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Semantic Levels Summary

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

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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

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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

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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

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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

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Example of CRM
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Example Mixed Use of DC and SWM Vocabulary

• ltswmactivity rdf about basensactivity
  /Textile Lengths 85-1002 Production"gt
• ltDCtypegtproductionlt/DCtypegt
• ltDCidentifiergtTextile Lengths 85-1002
  Production lt/DCidentifiergt
• ltswmbeginDategt1984lt/swmbeginDategt
• ltswmendDategt1985lt/swmendDategt
• ltswmlocateAt rdf resource "basens
  location/Ngkwarlerlaneme camp"/gt
• lt/ swmactivitygt

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Elements Relationships
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System Architecture
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Demonstration
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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

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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.

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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

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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.

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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.