Hendrik Thomas - Semantic Web - Topic Maps 2009

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

Introduction to Topic Maps
23/02/2009
Dipl.-Wirt.-Inf. Hendrik Thomas

University of Dublin
Trinity College

Trinity College, Knowledge Management HT 2009 1


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
reading cages of the Marsh’s Library, 1701

objective: efficient, comprehensive and simple access to knowledge



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
Associations

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

Topics Occurrences



Basic elements of a Topic Map (TAO)

● Topics (nodes)
represent all relevant subjects
Trinity

● Associations (edges) College

represent relations between the
subjects Hendrik Thomas

● Occurrences
connect subjects with relevant
information resources

CV

Steve Pepper: The TAO of Topic Maps - Finding the Way in the Age of Infoglut, 2002
http://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
Unique identifier in the topic map
<topic id=“123”> Can be system assigned
<baseName>
<baseNameString>Spanish Town</baseNameString>
</baseName>
<instanceOf>
Topic Name
<topicRef xlink:href=“#Town”/>
</instanceOf>
Topic Type
</topic>

<topic id=“Town”>
<baseName><baseNameString>Town</baseNameString>
</baseName>
</topic>
XTM Version 1.0 Standard
http://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




<topic id=“789”>
<baseName>
<baseNameString>Dublin</baseNameString>
</baseName>
Occurrence Type
<occurrence>
External Occurrence
<instanceOf> <topicRef xlink:href=“#portal”/> </instanceOf>
<resourceRef xlink:href=“http://dublin.ie” />
</occurrence>
<occurrence>
<instanceOf> <topicRef xlink:href=“#size”/> </instanceOf>
Internal Occurrence <resourceData>117,8 square km</resourceData>
</occurrence>
/topic>



Subject Identification
• subject locator (URL)

 if a topic represents a
Dublin -http://de.wikipedia.org /wiki/Ilmenau -
addressable object
»Topic« »Subject Locator« »Subjekt«
e.g. webpage, picture

• subject identifier (URL) »Subjekt Indicator«
Dublin
 if a topic represents a The capital of
the irisch nation

non-addressable object,
is Dublin,
situated at the
east cost of the
u--
e.g. a town, me, you »Subject Identifier«
i ki/ Il
me
na irland …

PSI rg /w
ia.o
i ped
 Published Subject Indicator (PSI) t p:/
/ de.
w ik
- -ht
create a common vocabulary Dublin
Dublin
»Topic« »Subjekt«



XRM 1.0 - XML Representation

<topic id=“Dublin”> Subject identifier
<subjectIdentity>
<subjectIndicatorRef
xlink:href="http://www.wikipedia.ie/dublin"/>
</subjectIdentity>
</subjectIdentity>
PSI
</topic>

<topic id=“DublinMap”> Subject locator
<subjectIdentity>
<resourceRef
xlink:href="http://www.wikipedia.ie/dublin/map.jpg"/>
</ resourceRef >
</subjectIdentity>
</topic>



Merging – automatic integration of Topic Maps
Topic Map A Topic Map B
»Subject-Identifier« »Subject-Identifier«
http://wiki.de/ilmenau http://wiki.de/ilmenau

two topics will be merged, Ilmenau »Topic« Ilmenau »Topic«

if both have an equal: »Occurrence Type« »Occurrence Type«
Zip Code
Postleitzahl Inhabitants

• subject identifier (PSI)
98693 26540
• subject locator »Internal Occurrence« »Internal Occurrence«

Topic Map C MERGING
• item identifier (topic id)
• … »Occurrence Type«

Zip Code
»Internal Occurrence«
98693 »Subject-Identifier«

»Internal Occurrence« Ilmenau http://wiki.de/ilmenau
26540

Inhabitants

Trinity College, Knowledge Management HT 2009 »Occurrence Type« 15


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)

pupil of
Alexander Philosopher
● associations are not directed the great Aristotle

● associations have roles
(similar to UML)

education-relation
Alexander the great Philosopher Aristotle

Role: pupil Role: teacher



Associations (3/4)

Alexander der Große Aristotle



Associations (4/4)

association type: parent relation

role: son role: mother role: dad

Tom Mary Paul



XTM 1.0 – XML Representation

<association id=“12345678”> Internal ID
<instanceOf>
<topicRef xlink:href=“#teacher-pupil-relation”/>
</instanceOf> Association
Type
<member>
<roleSpec>
<topicRef xlink:href=“#teacher”/>
</roleSpec>
<topicRef xlink:href=“#Aristoteles”/> Role Type
</member>
Role Player
<member>
<roleSpec>
<topicRef xlink:href=“#pupil”/>
</roleSpec>
<topicRef xlink:href=“#AlexanderTheGreat”/>
</member>
</association>


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



Scope
<topic id=“123”>
<baseName>
<scope>
<topicRef xlink:href=“#english”/>
</scope>
Topic Name
<baseNameString>Spanish Town</baseNameString>
</baseName>
</topic>
<topic id=“english”>
<baseName>
<baseNameString>English Language</baseNameString>
</baseName>
</topic>



Topic Maps = meta-layer for structuring a domain



Meta-Ontologies in Topic Maps

● Meta-Ontologies are build of: Ontology Level

● association types
pub is located in town is part of country

● role types

● occurrence types
O’brians pub Dublin Ireland

● name types

Instance Level

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



Process model for the
development of a topic map

Preparation
Implementation

Analysis Documentation

Rough Draft Detailed Draft



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
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
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

Sport

Sight
rugby field FIT
eis hall
city hall gym Garrett
Paul Durcan
Fitzgerald
Wicklow Ronan
Mountains Keating
Trinity
College
DUBLIN Sean
Lemass Thomas
Christ-
Parnell
church
Howth
Kilkenny Famous
London Persons
Cork

Cities



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
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

file:///C:/work_ibm/Dissertation/veröffentlichungen/07_Buch/Edit/type_samples_geo_form.jpg



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



3. Draft – Topic Types

Germany Dublin
Countries
Countries Towns
Cities Sightseens
Sights
Spire Cork
Germany Dublin Townhall
Killkenny Museum France Cork Museum

Townhall Ireland Killkenny Spire
Ireland
France



3. Draft – Topic Types

● one topic type is often not precise enough
● a topic can have multiple topic types

Ontology Level

Building Sight Irisch

Dublin Townhall Dublin

Instance Level



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

Working Place »Role Type«

»Topic Type« Mayor Job-Relation »Association Type«

Mayor »Role Type«

»Instance« Person Person



3. Draft: Topic Names
● every topic should have at least one name human readable name
● choice of appropriate / default labels for subject is difficult ! ! ! ! !

»Concept«

„Dublin“
»Object« »Term / Symbol«


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 roles
● occurrences types (external + internal)
● topic names
● find instances
(topics, associations, occurrences)




Preparation
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
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 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:xxxoks-samplersapache-tomcatbin
● 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.



Links
Topic Maps engines:
● TMAPI – Common Topic Map Application Programming Interface
http://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



End

Thanks for your attention!


Hendrik Thomas - Semantic Web - Topic Maps 2009

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