© 2001 empolis UK1 Topic Maps, NewsML and XML: Possible Integration and Implementations. By Soelwin...

© 2001 empolis UK 1

Topic Maps, NewsML and XML:Possible Integration and

Implementations.

By Soelwin Oo.


Introduction

Integration of Topic Map Technologies

K42 & NewsML

Implementation of Topic Map Technologies

Pitfalls of Topic Map Ontology Merging


Topic Maps

Capture concepts

Impose Knowledge on to Data

Create Knowledge Based Relationships

Powerful Knowledge & Resource Retrieval


NewsML

Developed by the IPTC

XML Based

Possess Topic Ontology Metadata

Content Driven Approach For Topic Map Navigation


NewsML & Topic Map Integration

Example IPTC Topic Set

<TopicSet Duid="iptc.importance" FormalName="Importance"> <Comment xml:lang="en">Relative significance of the metadata applied to a NewsComponent.</Comment> <Topic Duid="imp1"> <TopicType Scheme="IptcTopicType" FormalName="Importance"/> <FormalName Scheme="IptcImportance">High</FormalName> <Description xml:lang="en">The metadata is very important.</Description> </Topic> <Topic Duid="imp2"> <TopicType Scheme="IptcTopicType" FormalName="Importance"/> <FormalName Scheme="IptcImportance">Medium</FormalName> <Description xml:lang="en">The metadata is quite important.</Description> </Topic> <Topic Duid="imp3"> <TopicType Scheme="IptcTopicType" FormalName="Importance"/> <FormalName Scheme="IptcImportance">Low</FormalName> <Description xml:lang="en">The metadata is of low importance.</Description> </Topic></TopicSet>


NewsML & Topic Map Integration Topic Properties:

– Formal Name– Description– Topic Type

NewsML Topic ‘High’:

<Topic Duid="imp1"> <TopicType Scheme="IptcTopicType" FormalName="Importance"/> <FormalName Scheme="IptcImportance">High</FormalName> <Description xml:lang="en">The metadata is very important.</Description> </Topic>


FormalName

XTM representation:


<FormalName Scheme="IptcImportance">High</FormalName>

<topicMap> <topic id="t-IptcImportance "> <instanceOf> <subjectIndicatorRef xlink:href="http://www.TopicMaps.org/xtm/1.0/index.html#topic" /> </instanceOf> <baseName> <baseNameString>IptcImportance</baseNameString> </baseName> </topic>

<topic id="t-High"> <instanceOf> <subjectIndicatorRef xlink:href="http://www.TopicMaps.org/xtm/1.0/index.html#topic" /> </instanceOf> <baseName> <scope> <topicRef xlink:href="#t-IptcImportance " /> </scope> <baseNameString>High</baseNameString> </baseName> </topic></topicMap>


NewsML & Topic Map Integration Description

XTM representation:

<Description xml:lang="en">The metadata is very important.</Description>

<topicMap> <topic id="t-High">

<baseName> <scope> <topicRef xlink:href="#t-IptcImportance " /> </scope> <baseNameString>High</baseNameString> </baseName>

<baseName> <scope> <topicRef xlink:href="#t-Description " /> </scope> <scope> <topicRef xlink:href="#t-xml:lang=en " /> </scope> <baseNameString> The metadata is very important.</baseNameString> </baseName>

</topic></topicMap>


TopicType

XTM representation:

<TopicType Scheme="IptcTopicType" FormalName="Importance"/>

<topicMap> <topic id="t-High"> <instanceOf> <topicRef xlink:href="#t-Importance" /> </instanceOf>

<baseName> <scope> <topicRef xlink:href="#t-IptcImportance " /> </scope> <baseNameString>High</baseNameString> </baseName>

<baseName> <scope> <topicRef xlink:href="#t-Description " /> </scope> <scope> <topicRef xlink:href="#t-xml:lang=en " /> </scope> <baseNameString> The metadata is very important.</baseNameString> </baseName> </topic>

</topicMap>



NewsML & Topic Map Integration Instance/Type Relationships of ‘Importance’

Topic SetTopicBaseName:”Importance”Scope:”IptcTopicType”

TopicBaseName:”High”Scope:” IptcImportance”

TopicBaseName:”Medium”Scope:” IptcImportance”

TopicBaseName:”Low”Scope:” IptcImportance”

Instance

Instance

Instance


NewsML Topic Map Implementation

Currently only possess TopicType/Instance relationships

Topics can possess Occurrences to ‘Addressable Resources’

Relate NewsML articles as Occurrences of Ontology Topics

Implement Resource Retrieval Mechanism


NewsML Topic Map Implementation Methodology for TopicType/Instance driven

Resource Retrieval1. Obtain a NewsML document that is located in a uniquely

addressable location.(For example an URL)

2. Create this document as an Occurrence of all the Ontology Topics that occur within it.

3. From this document, list all the Topics that have this document as an Occurrence.

4. The user can then choose a particular Topic of interest from the list and select to retrieve other addressable Occurrences of

that Topic.



Content Driven Approach to Topic Map Navigation

User’s starting point is the Base Ontologies instantiated within NewsML article

Presentation of sets of related Topics

Retrieval of sets of related Resources



Topic Association Driven Resource Retrieval

Topic Association Powered Occurrence ‘Filtering’

Resource Channel creation


Create Topic Association Template ‘Channel’ Members:

Arcs:

Role >> Ontology Topic to be played by Topic Type >> Ontology Topic

Role >> Channel Topic to be played by Topic Type >> Channel Topic


from Channel Topic to Ontology Topic: includes the topic of

For example:Sport Channel <includes the topic of> Football

from Ontology Topic to Channel Topic: is assigned to

For example:Football <is assigned to> Sport Channel


NewsML Topic Map Implementation Instantiate ‘Channel’ Topic Association Template

For Example:

Associated with ‘Sport Channel’:FootballBasket BallCricketSporting CompetitionEtc…

Associated with ‘Business Channel’:NasdaqCompanyFT IndexEtc…

Associated with ‘John’s Channel’:NasdaqFootballKnittingEtc…



Process incoming NewsML documents

Create documents as Occurrences of Ontology Topics

Segregate Documents according to Ontology Topic Channel association

Display Channelled Documents



Football

FT Index

Knitting

Cricket

Nasdaq

NewsML News Feed

NewsMLProcessor

Sport Channel

Business Channel

John's Channel

Football

Cricket

Nasdaq

FT Index

Nasdaq

Football

Knitting

Process news feed

Filter documents according to

associated Channel Topic


NewsML Topic Map Implementation Create the individual documents as Occurrences

of their respective Ontology Topics.

Segregate the Occurrences of the Ontology Topics according to their associated Channel Topics.

Sports Channel

Football

Cricket

Channel Topic Ontology Topics NewsML Resources

Football

Cricket


Integration of Multiple Topic Maps Existence of additional Custom Vocabularies Potential for duplication of Topics

Water/H2O

H2

H2OWater

IceSteam O2

consists of consists of

IceSteam O2 H2

can exist as

can exist as consists of

consists of

Topic

Association


Integration of Multiple Topic Maps

Ontology Merging provides a richer network of Knowledge

Solutions for duplicate Topic identification– Unique Topic ID– Unique basename-scope pairs– Name/ID Mapping Tables– Common Reference Ontology

No panacea for Ontology Merging


Conclusion

Steps for XML Ontology capturing

Simple TopicType/Instance based Resource Retrieval

Topic Association driven Occurrence Filtering

Topic Map Ontology Merging

© 2001 empolis UK1 Topic Maps, NewsML and XML: Possible Integration and Implementations. By Soelwin...

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Transcript of © 2001 empolis UK1 Topic Maps, NewsML and XML: Possible Integration and Implementations. By Soelwin...