Hendrik Thomas - Semantic Web - Topic Maps 2009

Lecture Introduction to Topic Maps Dipl.-Wirt.Inf. Hendrik Thomas

1Trinity College, Knowledge Management HT 2009

Introduction to Topic Maps

23/02/2009Dipl.-Wirt.-Inf. Hendrik Thomas

University of DublinTrinity College



Outline

1. Part: Lecture (Today)

– Information retrieval challenges

– Introduction to Topic Maps

– Process model for the design Topic Maps

– Important modeling questions & guide lines

2. Part: Lab Exercise (Wednesday)

– Topic Maps Editor

– Modeling of a sample Topic Map



Information Retrieval Challenges

fundamental problems:

– information flood

gigantic amount of information (invisible web)

heterogeneity of data (txt, movie, pic)

– information deficiency

– information losses

objective: efficient, comprehensive and simple access to knowledge

reading cages of the Marsh’s Library, 1701



Introduction to Topic Maps

• Topic Maps is a ISO-standard for encoding knowledge & the connection with relevant information resources(ISO/IEC 13250: Topic Maps) http://www.isotopicmaps.org/sam/sam-model/

• objective of Topic Maps

formal modelling of relevant knowledge aspects and its structure

= semantic information

• domains of applications:

– organisation of information spaces (e.g. digital libraries)

– management of information and knowledge

– interoperability and exchange of knowledge



Origins of Topic Maps: book indexes

Gorda Sound see North SoundLittle Dix Bay .................... 89North Sound ....................... 90Road Harbour see also Road Town ... 73Road Town ...................... 69,71Spanish Town ................... 81,82Tortola ........................... 67Virgin Gorda ...................... 77

OccurrencesTopics

Associations



Basic elements of a Topic Map (TAO)

• Topics (nodes)

represent all relevant subjects

• Associations (edges)

represent relations between the subjects

• Occurrences

connect subjects with relevant information resources

Trinity

College

Hendrik Thomas

CV

Steve Pepper: The TAO of Topic Maps - Finding the Way in the Age of Infoglut, 2002http://www.ontopia.net/topicmaps/materials/tao.html



Topics

• topics are surrogates or “proxies” (inside the computer) for the subjects that you want to talk about

• subject can be anything = persons, objects, thoughts (abstract or real)

• each subject is represented by exactly one topic!

• objective: topics become “binding points” for everything that is known about a given subject

topic as a digital representation subject in the real world



Topic Names

• topics can have multiple names

• application can choose appropriate names depending on context

– more than one language

– synonyms

• examples: TU Ilmenau (patent documents)

– University of Applied Science in Ilmenau

– Technische Hochschule Ilmenau

– TU-Ilmenau

– TUI

– Technical University of Ilmenau



Topic Types

• topic types represent a group of similar topics

• a topic type represents the class of which the topic is an instance

• examples:

– Topic „Angela Merkel“ is of the topic type „human“

– Topic „Trinity College” is of the topic type „university“

• topic types are topics every topic can be modeled



XTM Version 1.0 – Topic Maps XML Representation

<topic id=“123”>

<baseName>

<baseNameString>Spanish Town</baseNameString>

</baseName>

<instanceOf>

<topicRef xlink:href=“#Town”/>

</instanceOf>

</topic>

<topic id=“Town”>

<baseName><baseNameString>Town</baseNameString>

</baseName>

</topic>

Topic Type

Unique identifier in the topic map

Can be system assigned

Topic Name

XTM Version 1.0 Standardhttp://www.topicmaps.org/xtm/



Occurrences

• occurrence model relations between information resources and topics

• occurrences can be everything (files, database tables, etc.)

• occurrences can be

– external: reference to an external resources = URL

– internal: key-value-pairs as part of the topic map

• occurrence types are topics





<baseName>

<baseNameString>Dublin</baseNameString>

</baseName>

<occurrence>

<instanceOf> <topicRef xlink:href=“#portal”/> </instanceOf>

<resourceRef xlink:href=“http://dublin.ie” />

</occurrence>

<occurrence>

<instanceOf> <topicRef xlink:href=“#size”/> </instanceOf>

<resourceData>117,8 square km</resourceData>

</occurrence>

/topic>

External Occurrence

Occurrence Type

Internal Occurrence



Subject Identification

-http://de.wikipedia.org/wiki/Ilmenau-

»Topic«

Dublin

»Subjekt«»Subject Locator«

• subject locator (URL)

if a topic represents a

addressable object

e.g. webpage, picture

Dublin

--http://de.wikipedia.org/wiki/Ilmenau--

DublinThe capital of the irisch nation is Dublin, situated at the east cost of the irland …

Dublin

»Topic« »Subjekt«

»Subjekt Indicator«

»Subject Identifier«

• subject identifier (URL)

if a topic represents a non-addressable object, e.g. a town, me, you

Published Subject Indicator (PSI) create a common vocabulary

PSI



XRM 1.0 - XML Representation

<topic id=“Dublin”>

<subjectIdentity>

<subjectIndicatorRef

xlink:href="http://www.wikipedia.ie/dublin"/>

</subjectIdentity>

</subjectIdentity>

</topic>

<topic id=“DublinMap”>

<subjectIdentity>

<resourceRef

xlink:href="http://www.wikipedia.ie/dublin/map.jpg"/>

</ resourceRef >

</subjectIdentity>

</topic>

Subject identifier

Subject locator

PSI



Merging – automatic integration of Topic Maps

two topics will be merged,

if both have an equal:

• subject identifier (PSI)

• subject locator

• item identifier (topic id)

• …

http://wiki.de/ilmenau

»Subject-Identifier«


Ilmenau

98693

Postleitzahl

»Internal Occurrence«

»Occurrence Type«



Ilmenau

26540

Inhabitants


»Occurrence Type«

Topic Map A Topic Map B


Ilmenau

MERGING

Inhabitants

Zip Code

»Occurrence Type«

»Occurrence Type«

26540

98693



Topic Map C

»Topic« »Topic«

Zip Code



Associations (1/4)

• associations represent relations between topics (subjects)

e.g. „Dublin“ is part of „Ireland“

• topics and associations constitute the semantic network

• associations have always one type („is_a“, „part_of”, etc.)

• association types are topics every association type can be modeled



Association Roles (2/4)

• associations are not directed

• associations have roles (similar to UML)

Philosopher

Aristotle

Alexander

the great

pupil of

Philosopher AristotleAlexander the great

Role: pupil Role: teacher

education-relation



Associations (3/4)

Alexander der Große Aristotle



Associations (4/4)

Tom PaulMary

association type: parent relation

role: son role: mother role: dad



XTM 1.0 – XML Representation

<association id=“12345678”><instanceOf>

<topicRef xlink:href=“#teacher-pupil-relation”/> </instanceOf>

<member> <roleSpec>

<topicRef xlink:href=“#teacher”/></roleSpec><topicRef xlink:href=“#Aristoteles”/>

</member>

<member> <roleSpec>

<topicRef xlink:href=“#pupil”/></roleSpec><topicRef xlink:href=“#AlexanderTheGreat”/>

</member></association>

Internal ID

Association Type

Role Type

Role Player



Scopes

• characteristics of topics:

topic names, occurrences and roles

• every characteristic is only valid in a specific context (scope)

• the name “Norge” for the topic Norway only in the language Norwegian

• an occurrence in the scope “user manual”

• an association “TM rule World” in the scope “fiction”

• scopes define valid context in Topic Maps

• benefits: context depended views + filter





<baseName>

<scope>

<topicRef xlink:href=“#english”/>

</scope>

<baseNameString>Spanish Town</baseNameString>

</baseName>

</topic>

<topic id=“english”>

<baseName>

<baseNameString>English Language</baseNameString>

</baseName>

</topic>

Scope

Topic Name



Topic Maps = meta-layer for structuring a domain



Meta-Ontologies in Topic Maps

• Meta-Ontologies are build of:

– association types

– role types

– occurrence types

– name types

NO STRICT SEPERATION – a topic can be a type and an instance at the same time

pub is located in town is part of country

Dublin IrelandO’brians pub

Ontology Level

Instance Level



Process model for the

development of a topic map

Preparation

Analysis

Rough Draft Detailed Draft

Documentation

Implementation



Who is involved in the modelling process?

Person Tasks

project manager control and coordination

Experts provide domain knowledge

User potential user of the Topic Maps based application

Editor maintenance and input of data in the topic map

Developer implements the Topic Maps based application

ontology expert analysis of the domain and design of the topic map

combines all provided information and resources



Model example

• task: develop an information portal for Dublin city

– a lot of tourism, historical and sports related attractions

– multiple information and information sources

» opening hours

» event plans

» descriptions

information are scattered and difficult to access

objective: model a topic map for this information portal



Preparation

Analysis


Documentation

Implementation





1. Phase: Preparation

• first steps:

– Define project goals and context of the project

– Collect all relevant information sources

– Workshops and interview with involved persons

• key question:

– Which knowledge domain should be modelled?

– Who are the potential users?

– Narrow down the modelling domain

define guidelines



1. Preparation: Narrow down the modeling domain

• focus: boarders of the modeling domain

– What should be modeled and what not?

– Example: geological structure of the city

• granularity: level of details of the modeling

– What details should be modeled – where to stop?

– Example: every house in Dublin or only relevant once

depend on knowledge domain as much as necessary but no more



1. Preparation: Project frame

Question Answer

knowledge domain

target group

focus

granularity

relevant sources



Preparation

Analysis


Documentation

Implementation





1. Phase: Analysis

• objective:

– identify detailed requirements

– analysis of the available information resources

• 1. source: structured data & unstructured data

– “Do not trust the schema/documentation”

– Murphy’s law

• 2. source: experts, file owner and user

– huge domain specific knowledge

– experts have very limited view = trained incapacity

– user provide open and praxis oriented view on problems

result of the analysis: collection of potential concepts



2. Analysis: Card-Sorting-Method

WicklowMountains

Thomas Parnell

Cork

KilkennyHowth

London

Trinity College

rugby field FIT

gym

eis hall

Christ-church

city hall

Sport

Sight

Cities

Famous Persons

Sean Lemass

Paul Durcan

Ronan Keating

Garrett Fitzgerald

DUBLIN



Exercise – Your turn …

• develop a short concept collection for the information portal

for Dublin city, please consider:

– relevant concepts

– relations between these concepts

– relevant classes



Preparation

Analysis


Documentation

Implementation





3. Draft: Types

• unspecific definition of types:

– is a collection of things which have something in common

– is a collection of individuals which share at least one common attribute

– a type groups topic-map-elements together into a category

where its elements are designated as instances of the type



3. Draft – Topic Types Hints

• a type has instances if a type has no instances it is not a type

• if you ask if “foo” is a type, then simply check,

if things exists which are “foos’”

• samples:

– Geography is a category but not a type = no instances

– Country is a type because things exists which are countries

= Ireland, France are countries



Countries

Germany

France

Ireland

Towns

Dublin

Cork

Killkenny

Sightseens

Townhall

Museum

Spire

Germany

France

Ireland

Killkenny

Dublin

CorkSpire

Museum

Townhall

3. Draft – Topic Types

SightsCitiesCountries



3. Draft – Topic Types

• one topic type is often not precise enough

• a topic can have multiple topic types

Ontology Level

Instance Level

Dublin Townhall

Building Sight

Dublin

Irisch



3. Draft – Topic Types vs. Role Types

• instances are inescapably bound to a type

from the beginning of its existence till the end

• samples:

– person is a type, because someone is a person or not

– man or woman are also (relative) good types

– town mayor is a bad type because a person acts as mayor

only during his election time frame

the rest of the time he is a “normal” person role type

it is sometimes difficult to separate topic types and role types



3. Draft: Topic-Typen vs. Role Types

Dublin

Person

Mayor »Role Type«

Working Place »Role Type«

Job-Relation »Association Type«

Person

Mayor»Topic Type«

»Instance«



3. Draft: Topic Names

• every topic should have at least one name human readable name

• choice of appropriate / default labels for subject is difficult ! ! ! ! !

»Term / Symbol«

„Dublin“»Object«

»Concept«



3. Draft: Create the network

• model instances of topics and associations

topics + associations = semantic network



Exercise – Your turn …

• develop a draft for a Topic Maps meta-ontology

assign appropriate Topic Maps element to the concepts

» topic types

» association types

» association roles

» occurrences types (external + internal)

» topic names

• find instances

(topics, associations, occurrences)



Preparation

Analysis


Documentation

Implementation





4. Documentation

• documentation of the meta-ontology of the Topic Maps

– list of the types + description

– Published Subject Identifier (PSI)

– Topic Maps graph supports understanding



4. Documentation

• Graphical Topic Map Notation (GTM)

– no official standard or commonly agreed method but

– use: GTMalpha Version 1.0 - see flyer!



Preparation

Analysis


Documentation

Implementation





5. Implementation – Topic Maps Exchange Formats

• objective:

– represent Topic Maps elements in a

– computer processable and exchangeable format

• many different exchange formats obstacle to the TM development

– LTM – Linear Topic Map Notation

– XTM – XML Topic Maps (Version 1.0 and 2.0)

common standard for the exchange of topic maps



5. Implementation – Topic Maps Editors

• (unfortunately) only few usable and useful tools

major disadvantage of Topic Maps

• Topic Maps editors:

– TM4L – Topic Map 4 E-Learning (open source)http://compsci.wssu.edu/iis/nsdl/download.html

– tmedit – Topic-Maps-Editor (open source)http://tmedit.org/

– Ontopia Knowledge Suit (commerical but powerful)http://www.ontopia.net/download/freedownload.html



5. Implementation – Ontopia Knowledge Suite (OKS)

• OKS is a collection of different JAVA applications

– Omnigator – text based navigator

– Ontopoly – Ontology editor

– Vizigator – graph visualiser



Exercise for the next lab (1/2)

• Develop a draft for a Topic Maps ontology for a choose domain

– Create a project frame

(objective, domain, focus, granularity, target groups)

– Develop a concept collection

– Assign appropriate Topic Maps element to the concepts

» topic types

» association types

» association roles

» occurrences types (external + internal)

» topic names

– Find at least 1 instance for every type



Exercise for the next lab (2/2)

• Download the OKS and task describtion

https://www.cs.tcd.ie/~thomash/

• Install the OKS and start the file startup.bat in the folder

C:\xxx\oks-samplers\apache-tomcat\bin

• Start a web browser and open the following site

http://localhost:8080/

• Tasks:

– Model your topic map draft

– Export your topic map (XTM 1.0 + Topic map without schema)

• Create a second Topic Maps and merge both Topic Maps



Summary (1/2)

• ontology design is an art, not a science

– there is no single correct answer

– practice makes perfect

• many of the decisions depend on external factors

– the use of the ontology

– the organization that is going to use it

– systems connected to the topic map application

– the tools used on the application

• when in doubt, go for beauty



Summary (2/2)

• advantages:

– Topic Maps born out of long tradition of indexing

– Simple concepts: Topics, Associations, Occurrences

– Human friendly modeling approach: “map”

– Designed with merge ability in mind

– Concise vocabulary: approx 21 elements

• disadvantages

– only few use-cases & no killer application

– not enough tools and APIs

– Very small but passionate community, good commercial backing

Topic Maps will rule the world but not yet!



Literature

• Lars Marius. Garshol, G. Moore: ISO/IEC JTC1/SC34, Information Technology – Document Description and Processing Languages, http://www.isotopicmaps.org/sam/sam-model/, 2006.

• Steve Pepper: The TAO of Topic Maps: Finding the Way in the Age of Infoglut. http://www.ontopia.net/topicmaps/materials/tao.html, 2002.

• Steve Pepper, Graham Moore: XML Topic Maps (XTM) 1.0, http://topicmaps.org/xtm/index.html, 2002.

• Lutz Maicher, Alexander Sigel und Lars M. Garshol (Hrsg.): Leveraging the Seman-tics of Topics Maps: Second International Conference on Topic Maps Research and Applications, TMRA 2006, Leipzig, Germany, October 2006, Revised Selected Papers, Berlin - Heidelberg 2007.



LinksTopic Maps engines:

– TMAPI – Common Topic Map Application Programming Interfacehttp://www.tmapi.org/

– tinyTIM – in memory TMAPI implementation

Topic Maps editors:

– tmedit – Topic-Maps-Editor (open source)http://tmedit.org/

– Ontopia Knowledge Suit (commercial)http://www.ontopia.net/download/freedownload.html

– TM4L – Topic Map 4 E-Learning (open source)http://compsci.wssu.edu/iis/nsdl/download.html



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Hendrik Thomas - Semantic Web - Topic Maps 2009

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