COMP 6703 eScience Project Semantic Web for Museums Student : Lei Junran Client/Technical Supervisor...

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 DATAJohn Smith Name222 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/

GreeceSubject

&ns;/location/

EuropeObject

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.