COMP 6703 eScience Project
Semantic Web for Museums
•
Student : Lei Junran
•
Client/Technical Supervisor : Tom Worthington
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Academic Supervisor : Peter Strazdins
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Period : 2006 Semester 1
What
is in my presentation
•
Motivation
•
Objectives
•
Technologies
•
Design Considerations
•
Demonstration
•
Conclusion
•
Future Work
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
Museums System Example
Museums System Example
Museums System Example
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.
Project Objectives
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Current Objective
-
to develop an
effective semantic web archive
system for museums.
•
Long Terms
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research the promising
semantic technology for creating the
knowledge management network
among museums.
Technologies
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What is Semantic Web
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Tim Berners
-
Lee's original web vision
involved more than retrieving
Hypertext Markup Language (HTML)
pages from Web servers.
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Make the web a more collaborative
medium.
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Create a web of data that machines
can process
How to make Semantic Web
possible?
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Make the data smarter.
–
application
-
independent, easily
discovered, to be described with
concrete relationships…
Four Levels of smart data
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Text Documents and Database Records
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Data just can be used in a single application
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XML documents using single vocabulary
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Data is now smart enough to move between
applications in this museum
.
•
XML documents with mixed vocabularies
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Data can be composed from multiple
museums or institutes
Four Levels of smart data
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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
Semantic Web Elements and
technologies
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Metadata
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XML
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RDF
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Ontology
Metadata
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Meta
-
data: meaning of data values;
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Example
:
DATA
META DATA
John Smith
Name
222 Happy Lane
Address
XML
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XML(Extensible Markup Language) is
the syntactic foundation layer of the
Semantic Web.
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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>
XML Metadata benefits
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All data are described with a set of
predefined vocabulary and syntax.
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Enable exchange, interoperability,
information integration and
application independence.
RDF
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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
RDF/XML Data Example
<swm:location rdf : about = "&ns; /
location /
Greece
">
<swm:
locationAt
rdf:resource = "&ns;
/ location /
Europe
"/>
</swm:location>
What are included in Ontology?
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Classes: Object, Activity, Location
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Relationships: object <locate at> location,
company <is a > organization
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Properties: Identifier(cardinality 1:1),
Type, Creator
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Constrains and Rules: If X is true, then Y
must also be true.
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Functions and Process:
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A formal vocabulary (defined terms) for all
above
Ontology Languages
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Ontology is represented in knowledge
representation languages
–
RDFS (lightweight ontology)
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Elements: Class, label, subclassOf, Property,
Domain, range, type, subPropertyof…
–
OWL (Robust ontology)
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Elements: RDFS plus someValuesFrom
∃
,
allValuesFrom
∀
, hasValue
∋
, minCardinality ≥,
cardinality =, intersectionOf, unionOf…
Why Use Ontology
•
defines the domain vocabulary.
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Improve association expression,
interoperability
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Ontology languages are backed by a
rigorous formal logic, which makes
the ontology machine
-
interpretable.
Semantic Levels Summary
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Semantic Levels (Redrawn after C. Daconta, et al 2003)
Design Considerations
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Use existing ontology
–
CIDOC
CRM
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CIDOC
:
The
International
Committee
for
Documentation
of
the
International
Council
of
Museums
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CRM
:
Conceptual
Reference
Model
•
A
domain
ontology
for
cultural
heritage
information
Design Considerations
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Use existing metadata standard
–
Dublin Core
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A simple yet effective element set for
describing a wide range of networked
resources.
•
Simplicity, Commonly understood
semantics, Extensibility
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Example Elements: Identifier,
Description, Format, Date, Creator…
CIDOC CRM
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Advantages
–
Comprehensive and widely accepted
–
Mappings have been established with
major metadata standards
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Disadvantages
–
Includes 81 classes and 132 properties
–
Vocabulary is too detailed to be used as
metadata directly
Solutions
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Use subset of CRM
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Use Dublin Core Metadata Standard
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Redesign the vocabulary of the
applied subset when DC can not
express the meaning of the subset.
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Use DC and subset vocabulary (SWM
vocabulary) as metadata
Example of CRM
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>
Elements Relationships
System Architecture
Demonstration
Conclusion
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A
semantic web prototype system
has
been developed
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A RDF Schema has been designed
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The museums collections could be
input and transferred to RDF data
for preservation
Conclusion
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Data is now smart enough to be
described with concrete relationships
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RDF data output and Batch input
increases the interoperability with
other semantic systems and provide a
convenient transfer way to existing
data.
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
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.
Future Work
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Redesign Ontology using robust
ontology language (eg. OWL)
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Add more constrains and rules for
inference
•
Design system showing more benefits
of semantic web technology
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Web Services and Taxonomies in
Semantic Web.
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