Semantic Web for Museums

steelsquareInternet and Web Development

Oct 20, 2013 (3 years and 7 months ago)

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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.