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

•
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

•

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.

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


How to make Semantic Web
possible?

•
Make the data smarter.

–
application
-
independent, easily
discovered, to be described with
concrete relationships…


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


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

Semantic Web Elements and
technologies

•
Metadata

•
XML

•
RDF

•
Ontology


Metadata

•
Meta
-
data: meaning of data values;


•
Example
:



DATA



META DATA



John Smith


Name



222 Happy Lane

Address






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>

XML Metadata benefits

•
All data are described with a set of
predefined vocabulary and syntax.

•
Enable exchange, interoperability,
information integration and
application independence.




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

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?

•
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



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…

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.


Semantic Levels Summary










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

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

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…






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


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

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

•
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


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.



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

•
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.