Topic Maps for the Three Kingdoms: The Many Applications of Topic Maps

Topic Maps
for the Three
Kingdoms
The Many
Applications of
Topic Maps
AToMS, Seoul, June 2006
Steve Pepper
Chief Strategy Officer, Ontopia
Convenor, SC34/WG3
Editor, XML Topic Maps
<pepper@ontopia.net>

http://www.ontopia.net/© 2006 Ontopia AS
What I Will Talk About
• What are the Three Kingdoms?
• Advantages of Topic Maps
• Major Application Areas of Topic Maps
– Semantic Indexing
– Information Integration
– Knowledge Management & eLearning
• National Knowledge Base for Korea
• (Topic Maps and the Semantic Web)
• Conclusion

The Three Kingdoms
1. Silla, Goguryeo, Paekche:
the Three Kingdoms of early Korean history
– Topic Maps have something to offer for everyone
in Korea: A National Knowledge Base for Korea
would be a wonderful thing
2. Industry, Public Sector, Education & Research
– Topic Maps can be applied across every sector)
3. Semantic Indexing, Information Integration,
Knowledge Management/eLearning
– Topic Maps cover a wide variety of application areas
4. Norway, Netherlands, and ... USA
– Well, USA is not a kingdom, but it could have been…

Key Advantages of Topic Maps
• Topic Maps provides a subject-centric,
associative model for representing knowledge
• The basic ideas are not new
– They are familiar from library science, cognitive psychology,
artificial intelligence, etc.
• But the key advantages are new
– ISO standard – vendor independence, longevity
– Formal data model – machine processable
– Interchange syntax – use across multiple systems
• Let’s see how to apply these advantages...

Semantic
Indexing

Semantic indexing
• We are drowning in an information tsunami
– Everyone faces the same problem: How to find the information
• Many people believe search engines are the solution...
– ...but they only provide partial alleviation
• Topic Maps provides a better solution
– Subject-based organization (everything organized around topics)
– Associative model (very intuitive navigation)
– Structured queries (adds more power to full-text search)
• Semantic indexing applications
– Taxonomy management
– Metadata management
– Semantic portals

Taxonomy Management
• Addresses the problem of managing unstructured content
– Solution is to organized by subject –that’s how users search
– Many companies understand need to use taxonomies
• A taxonomy is a simple form of topic map
– Topic Maps provides subject-based organization de-luxe
• Using Topic Maps offers many benefits:
– Associative model allows for evolution beyond simple hierarchies
– Taxonomy can also be used as a thesaurus, a glossary or an index
• This capability can also be added to Content
Management Systems
– (see other presentations later today)

• Norwegian Government Administration Services
metadata server
– Manages metadata for official
publications using Topic Maps
– Ensures consistency
– Used in the central public
information portal (ODIN)
• The system provides
– Authoring system for editors
– Vocabulary Editor
– Metadata Export
– Web Services
– Unique identifiers for
documents
Metadata Management
ODIN
Lovdata
Exported subjects ASCII-export
Metadata
server
MUP
Indexes
Engine
ODIN
Meta-
data
…
Search engine
Logistics

Semantic portals
• Basic principals
– Site structure is defined as a topic map
– The topic map ontology IS the Information Architecture
– Each page represents a topic (i.e., subject-centric)
– User-friendly navigation paths defined by associations
– Topics used to classify content (replaces hierarchical
classification)
• Portals can be connected using remote access
protocols to exchange topic map fragments
– Can evolve over time into a Knowledge Management solution

Portals Powered by Topic Maps
• Information
Architecture
for web
applications
• Used for
web sites,
portals,
corporate
intranets,
etc.
ClientClient
Portlet
Portlet
Portlet
Portlet
Context Topic Map
Portlet
Subsystem Subsystem Subsystem
Portlet

Many Such Portals in Norway
• One example:
The Works of Henrik Ibsen
• Famous Norwegian dramatist
– Wrote Peer Gynt, Hedda Gabler,
A Doll’s House, etc.
– A national icon for Norway
– Died 1906: This year is 100 year
anniversary
• 8-year project to digitize everything
he wrote (plays, letters, articles, etc.)
• Semantic indexing for the online
version is based on Topic Maps

For General Users and Specialists
XML
Navigasjon og
aggregert informasjon
Assosiativ navigasjon,
tidslinje, indeks over verk,
personer, steder, m.v.
Tekstmateriale – verk,
varianter og kommentarer
Hovedtekster, noter og
kommentarer, varianter
og variantsammenligningXML-databasen
Emnekart
Kunnskapslag
Allmen interesse,
verdifullt for alle brukere
Vitenskapelig orientert
Høy verdi for forskere
Delvis utilgjengelig for andre
XML
Navigasjon og
aggregert informasjon
Assosiativ navigasjon,
tidslinje, indeks over verk,
personer, steder, m.v.
Tekstmateriale – verk,
varianter og kommentarer
Hovedtekster, noter og
kommentarer, varianter
og variantsammenligningXML-databasen
Emnekart
Kunnskapslag
Allmen interesse,
verdifullt for alle brukere
Vitenskapelig orientert
Høy verdi for forskere
Delvis utilgjengelig for andre
Knowledge layer
General interest
Valuable for all users
Scientific orientation
High value for researchers
Partly closed for others
Navigation and
aggregated information
Associative navigation,
timeline, index of works,
people, places, etc.
Text material – works,
variants, commentaries
Main texts, letters, notes,
commentaries, variants
and comparative material
Topic Map
XML database

sent year published year
mentions
sent to
mentions
sent from
Place
description
Rome
Letter
Letter to
Bjørnson
Person
description
Bjørnstjerne
Bjørnson
Person
description
Clemens
Petersen
Work
Peer Gynt
Rome
Letter to
Bjørnson
Bjørnstjerne
Bjørnson
Clemens
Petersen
Peer Gynt
Year 1867

sent year published year
mentions
sent to
mentions
sent from
Rome
Letter to
Bjørnson
Bjørnstjerne
Bjørnson
Clemens
Petersen
Peer Gynt
Year 1867

Catilina (1850)
Catilina er utgitt i to ganger, først i 1850, og senere i en bearbeidet
versjon i 1875. Forskjellene mellom de to tekstene er betydelige, og
de regnes derfor som to separate verk.
Ibsens brev om Catilina
Catilina omtales i flere av Ibsens brev:
• Brev til Ole Carelius Schulerud
(15.10.1849)
• Brev til Ole Carelius Schulerud
(05.01.1850)
• Brev til Kong Karl 15. (10.03.1863)
• Brev til Peter Hansen (28.10.1870)
Les Catilina (1850)
Dette er en forsiktig omarbeidet
versjon av grunnteksten med
rettelser og noter fra prosjektets
tekstforskere.
• Første akt
• Annen akt
• Tredje akt
• Fjerde akt
Ibsen skrev også en senere versjon
av Catilina: Gå til Catilina (1875)
Tekstarkiv
Følg tekstutviklingen til Catilina:
• 5 manuskripter
• 6 utgaver
• Faksimiler
• Sammenligning av tekstkilder
Gå til tekstarkivet
Kommentarer og innledning
Bakgrunn og informasjon om Catilina
fra prosjektets tekstforskere:
• Bakgrunn
• Tilblivelse
• Utgivelse
• Oppførelse
• Tekstkritisk redegjørelse
• Manuskriptbeskrivelse
• Litteraturliste
• Tillegg
Fra uroppførelsen av Catilina ved
slik og slik teater, sted i 1851.
Bildearkivet inneholder 32 bilder
tilknyttet Catilina (1850):
Gå til bildearkivet
Hva skjedde i 1850
Geografi
• Kristiania
Dikt
• I natten
• Guldharpen
• Bjergmanden
(totalt 12 dikt)
Brev
• Til kong Oscar 1. (12.07.1850)
• Til Clara Ebbell (nyttår 1850/51)
• Til Cathrine Martini (12.04.1850)
(totalt 23 brev)
Se alt som skjedde i 1850

Bjørnson, Bjørnstjerne Martinius
1832-1910, norsk forfatter
Prestesønn. Født i Kvikne (nå Tynset), Hedmark og flyttet 1837 til
Nesset sogn i Romsdal, der familien bodde til 1853, da faren
overtok Søgne prestekall. Etter skoleår i Molde reiste han 1850 til
Kristiania. Elev på "Heltberg Studentfabrikk," der han møtte Ibsen,
Lie og Vinje. 1852 examen artium med karakteren non. Vinteren
1852-53 huslærer for sine søsken hjemme på Nesset. Høsten 1853
leste han til anneneksamen, men avbrøt studiet og bestemte seg for
å bli dikter. Prestesønn.
Les mer om Bjørnstjerne Bjørnson ...
Ibsens omtaler av Bjørnson
Bjørnson omtales i flere tekster:
• Brev til bernhard dunker 22. juni 1864
• Brev til bernhard dunker 7. mars 1864
• Brev til christian tønsberg 11.
november 1866
• Brev til clemens petersen 10. august
1863
• Brev til den gyldendalske boghandel
5. mars 1871
• Brev til det kongelige norske
videnskabers selskab 25. mars 1865
• Brev til edvard grieg 24. august 1866
• Brev til frederik hegel 11. april 1870
• Brev til frederik hegel 12. februar 1870
• Brev til clemens petersen 10. august
1863
• Brev til den gyldendalske boghandel
5. mars 1871
Brev fra Ibsen
Mottok følgende brev fra Ibsen:
• Brev til Bjørnstjerne Bjørnson
(15.10.1854)
(15.10.1855)
(15.10.1856)
(15.10.1857)
(15.10.1858)
Brevenes innhold
Ibsen tar opp en rekke ulike emner
i sine brev til Bjørnson:
Personer
• Bjørnson, Karoline (8)
• Dunker, Bernhard (5)
• Ibsen, Susanna (4)
Vise alle ...
Steder
• Christiania Theater (7)
• Norge (5)
• Stortinget (4)
Vise alle ...

Pansori Web Site
• Created by INEK
– Korean folk music
• Subject of a
usability study
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
3 0 0
T 1 T 2 T 3 T 4 T 5 T 6 U s e r
Q u e ry
M e a n
C P R S
T M P R S
www.inek.co.kr

Enterprise
Information
Integration

Information integration
• Information is often
spread across
multiple systems
• Use Topic Maps to
provide a single
point of access to
all information
• You can avoid
costly migration
and re-engineering

EII: Why Topic Maps are Ideal
• Very flexible data model
– Hierarchical (XML), relational (RDBMS), associative (RDF) data can
be easily mapped to topic maps
• Topic maps can be merged
– You can generate topic maps from structured data
– You can classify unstructured content topic map taxonomy
– And then you can merge the topic maps to provide a unified view
• Topic maps can be filtered
– You can create personalized views of the unified information
• Advantages:
– Consolidated access to all related information
– Does not require migration of existing content
– Standards-based

http://www.ontopia.net/© 2006 Ontopia AS Source repository
T
Elmer
Bug database
C++ class
T
Bug
T
Requirement
caused by
breaks
Requirements DB
Example: Starbase Elmer
• Project is to build a server for integrating software information
• Multiple applications hold related data
– Unified topic map layer on top allows searching across repositories
– Provides data integration without changing the underlying applications
• Access to information provided through a portal
– Straightforward navigation interface
– Structured queries
• Topic map drives
integration with MS
Office Smarttags
– Elmer terms are highlighted
– Appear as links back into the
portal

Knowledge
Management

Knowledge Management / eLearning
• Every organization faces the challenge of managing knowledge
– Capturing and sharing knowledge in people’s heads
• Why are Topic Maps ideal for this?
– Because the model captures knowledge
– Because topic maps can be merged
– And because it is an ISO standard
• Some examples (there are many more)
– Business process management
– Product configuration
– Business rules management
– IT asset management
– Manufacturing asset management
– Intelligence gathering and analysis

Business Process Modelling
• Managing business process models
– The flexibility of the Topic Maps model allows arbitrary relationships
to be captured easily
• Processes are modelled in terms of
– The steps involved, their preconditions, their successors, etc
• Can be related through
– Composition (one process is
part of another),
– Sequencing (one process is
followed by another),
– Specialization (one process is
a special case of a more
general process)

Product Configuration
• Using topic maps to manage complex product configuration
for mobile phones
– Products belong to families
– Features belong to products or product families
– Features are grouped in feature sets
– Dependencies between features; different geographic applicability
• Network of dependencies is already quite complex
– Versioning makes it much worse
– Managing all this data is not easy
• Dependencies modelled in a topic map
– Product configuration engineers use this to
configure products using a very user-friendly interface
• Allows integration with product documentation
Features
Product
families
Products
System
data
Versioning

Business Rules Management
• Managing guidance rules for security classification
– Information about the production of nuclear weapons
– Subject to 1,000s of rules published in 100s of documents
– Most documents are derived from more general documents
• This complex web of relationships is captured
in a topic map
– Concepts are connected to if-then-else rules
– This constitutes a knowledge base
• Inference engine automatically
– classifies information
– redacts information
Guidance
topic
Parent
topic
Child
topic
Master
topic
Derived
topic
Concept
Responsible
person
Workflow
state

IT Asset Management
• Management of IT assets at University of Oslo
– Servers, clusters, databases, etc described in a TM
• This is used to answer questions like
– Service X is down, who do I call?
– If I take Y down, what else goes?
– If operating system Z is upgraded,
what apps are affected?
• System driven by composite
topic map
– Partly autogenerated
– Partly handcoded
XTM RDBMS
backend
usit.ltm
(handcoded)
oracle.ltm
(generated)
• Syntax control
• OKS schema
validation
• Versioning with
CVS
CVS
Topic Map Engine
Navigator framework
OKS API
UIOTM FW
Houdini Whitney

Asset Management: Manufacturing
• US Department of Energy uses
Topic Maps to “map” its
production facilities for nuclear
weapons
• The purpose is to get an
overview of
– equipment,
– processes,
– materials required,
– parts already built,
– etc.
• Cannot show you a screen shot
– Otherwise I will be shot 

Intelligence Agencies – Lots of Data
• Everything is
interconnected...
• The problem for intelligence agencies is to
find, record and utilize the connections
Islamic
Salvation
Foundation
July 22, 2004
in
Baghdad,
Iraq
Iraq
Role: Country
Base
of
Operations
for
Al Qaeda
Role: Terrorist
Organization
Location of
Method of
Attack Beheading

Merging Knowledge Models
USS
CHEYENNE
(SSN 773)
Currently
anchored
in
Role: Vessel
Knowledge
maintained by ONI
Has
active
agents of
Lebanon
Role: Country
Hamas
Role: Terrorist
Group
Knowledge Maintained by
another intelligence organization
Beirut
Role: City
Located
In
Beirut
Role: City

Merging Knowledge Models
• Knowledge models are merged
from separate applications,
between departments, or
between organizations.
• Knowledge is maintained by the
experts closest to it, but merged
freely to create models beyond
the expertise of a single group.
• Knowledge-driven applications
are enhanced as new models are
merged into the solution.

Tying All the Data Together
Knowledge Layer
<data>
<country>
</country>
. . .
</data>
Weapons
database
Vessel
Specification
Documents HTML
Documents
portal
pda
custom
app
web
Software
Code

eLearning
Applications

eLearning applications
• Knowledge is at the core of eLearning
– The acquisition of knowledge by humans
• Topic Maps provides an
excellent model
• It can be used to
– Capture what the pupil has learned
– Structure eLearning systems
– Organize school and university curricula
– Tie all of this together

Competency
goals
GradeTopic Individual
goals

Subject (with code)
Integration with Course Management Systems
via the subject (here: “Naturfag” = “Science”)

Integration with portals offering career advice
or information about further education courses

Vilbli.no
Udir.no
Skolenettet.no
You name it...
Pupils
learning platform
Local curricula
Integrating the Education System

E-learning
• Topic maps are associative
knowledge structures
– They reflect how people
acquire and retain knowledge
• BrainBank is used by
students to describe what
they have learned
– Initial users are 11-13 year
olds who have no idea what a
topic map is…
– They capture the key
concepts, name them,
describe them, and associate
them with others

Some Benefits of BrainBank
• Enables pupils to
– Capture the essence of their new
knowledge,
– Describe what they have learned,
– Keep track of their knowledge, and
– Lets the teacher help them
• Encourages a new way of learning
– Pupils construct knowledge by
“fitting” it together with existing
knowledge
– This is like adding a topic to a topic
map and associating it with pre-
existing topics

A National Knowledge Base
• Our experience from Norway
– As more Government Agencies adopt Topic Maps, a
distributed National Knowledge Base starts to emerge
• Topic Maps can be merged on-the-fly so that
knowledge is connected across agencies
• Published Subjects can be the infrastructure on
which a National Knowledge Base is built
– A Semantic Superhighway of unique topic identifiers
– Foundation for Topic Maps and Semantic Web

The National Knowledge Base
SNL
SNL
SNL
SNL
Skien
kom-
mune
Cap
Lex
Cap
Lex
NBL
Henrik
Ibsen
Hedda
Gabler
Skien
Et dukkehjem
A doll’s house
wrote
born in
wrote
“real world”
topic map
information
knowledge
Other Topic Maps
can be merged in
Ibsen-
senter
Ibsen-
senter
Ibsen-
senter
Ibsen-
senter
Ibsen-
senter
Ibsen-
senter
Et dukkehjem
HelmerHelmer
Dr. RankDr. Rank
Mrs. LindeMrs. Linde
KrogstadKrogstad
NoraNora

Conclusion
• Topic Maps can be used for many different
purposes in industry, public sector, and academia
– The principal applications are in semantic indexing,
information integration, knowledge management, and e-
learning
– The benefits can be very great
• With a carefully planned strategy, Topic Maps can
lead to the emergence of a National Knowledge
Base in the Three Kingdoms

Topic Maps and the Semantic Web
• Some people think RDF/OWL and Topic Maps are competitors
– (RDF/OWL are languages of the Semantic Web)
• Semantic Web gets much more publicity
– Partly because the W3C can bask in the glamour of the Web
– Partly because RDF and OWL appeal more to academics
• Why the perceived competition with Topic Maps?
– Partly because RDF/OWL and TMs have a number of similarities
– Partly because they stem from rival organizations (W3C and ISO)
– Partly because there are a few bigots (in each camp)
– Mostly because people do not fully understand the difference

Similar in Many Ways
• Both “extend” XML into the realm of semantics
• Both allow assertions to be made about subjects in the outside
world
• Both define abstract, associative (graph-based) models
• Both are intensely concerned with “identity”
• Both allow some measure of inferencing or reasoning
• Both have XML-based interchange syntaxes
• Both have constraint languages and query languages
• ...
• But they are also different in some crucial respects…

The Most Crucial Differences
• RDF/OWL is for machines;
Topic Maps is for humans.
• RDF/OWL is optimized for inferencing;
Topic Maps is optimized for findability.
• RDF/OWL is based on formal logic;
Topic Maps is not based on formal logic.
• RDF/OWL is to mathematics as
Topic Maps is to language.

RDF or Topic Maps: Some Rules of Thumb
• Do you simply want to encode document metadata?
– RDF is ideal and you won’t need OWL
• Do you want to achieve subject-based classification of content?
– Topic Maps combines flexibility and user-friendliness
• Do you want both metadata and subject-based classification?
– Go straight for Topic Maps, because it also supports metadata
• Do you want to develop agent-based applications?
– Use RDF/OWL; if you already have Topic Maps, it’s a good start
• Whatever you choose, know that you can move your data between
Topic Maps and RDF, thanks to the RDFTM work…

The RDF/TM Task Force
• A W3C task force supported by the ISO Topic Maps group
– Chartered to address RDF/Topic Maps interoperability
– Working within the Semantic Web Activity of the W3C
• Survey of RDF/Topic Maps Interoperability Proposals
– http://www.w3.org/TR/rdftm-survey/
• Guidelines for RDF/Topic Maps Interoperability
– http://www.ontopia.net/work/guidelines.html (draft)
• Focus on data interoperability:
– “The primary goal of these Guidelines is to enable data to be
translated from one form to the other without unacceptable loss of
information or corruption of the semantics. Further goals are to be
able to query the results of a translation in terms of the target model
and to share vocabularies across the two paradigms.”

Topic Maps for the Three Kingdoms: The Many Applications of Topic Maps

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