Topic Maps - Human-oriented semantics?

Topic Maps – semantics for humans?
WNRI Seminars on Semantic Technologies, 2010-12-15
Lars Marius Garshol
<larsga@bouvet.no>
http://twitter.com/larsga

Agenda
What are semantic technologies?
Introduction to Topic Maps
Topic Maps and classification
A short history of Topic Maps
The standards
Topic Maps and RDF
Example applications
Software
Learn more

What makes a technology semantic?

Semantics?
Semantics
the study of meaning (orig. the meaning of words)
Semantic technologies
describe not just data, but also the meaning of data
in traditional technology meaning is only in code and human interpretation
John Searle, "The Chinese Room"

Non-semantic data
What is this?
How many entities are represented here?
What is entities and what is properties?

The schema
People often say that the schema defines the semantics
But it's not really very semantic, is it?
XML is not a semantic technology

Topic Maps example
Separates entities from properties
Relations are clearly visible
We know the names of all entities
Can query for all 人 and get all instances
The full meaning remains obscure
Types
人
subtype
subtype
男性
女性
出来事
type-instance
type-instance
源氏
夕顔
参加
参加
夕顔との出会い

Semantic technology
Far richer description of concepts
arbitrarily complex description of classes and properties
Vocabularies can be reused across applications
Data can be automatically merged
Some of the meaning in the data can be modelled

Semantic technologies
Topic Maps
ISO standard, much used in portals
emphasis on "human" semantics
RDF
W3C standard, foundation of the "semantic web"
heavy use of logic in the stack of standards
Other alternatives
many other technologies want to be seen as semantic; how many of them are is disputable
only widely-accepted standards really matter

What are Topic Maps?
Uses for Topic Maps
Introduction

What is Topic Maps?
A technology for knowledge integration
describes concepts and their relations
allows documents to be attached to the concepts
concepts can be matched across different topic maps
matching allows topic maps to be merged seamlessly

What can Topic Maps be used for?
Primary usage
organizing information so you can find what you are looking for
common example: portal or intranet
less common: online publishing
However, Topic Maps is really just a way to organize information
can therefore be used for nearly anything
Other uses
e-learning
real knowledge management
decision support systems
...

From documents to topics
The TAO of Topic Maps
How to make a topic map

How to find the needle in this haystack?

The Topic Maps approach
(index)
(content)
topic map
documents
Create a conceptual map of the information being organized
concepts and relations
connections to documents (landscape)
Like a book with an index
or landscape and a map

Creating a topic map
Analyze the documents
Select the key concepts (topics)
Analyze the key concepts (topic types)
Identify their relationships (associations)
For each topic, connect relevant documents (occurrences)
Voila!

1. Document analysis
Key concepts
What is it?
Evaluation report from the MODE project
MODE, (Evaluation)
CV of Jane Doe
Jane Doe, (CV)
Budget for IT group
IT group, (Budget)

2.-3. Topics, with types
Person
Department
Project
Jane Doe
IT group
MODE

4. Adding associations
employed in
worked on
part of
worked on
Consumer
products
part of
employed in
Documentation
Roger Roe
Jane Doe
IT group
MODE

5. Adding occurrences
Jane Doe
CV
budget
evaluation
worked on
employed in
IT group
MODE

6. The TAO of Topic Maps
worked on
MODE
Jane Doe
Topics
represent things of interest
Associations
represent relations between topics
Occurrences
connect topics to information resources with relevant information

How to find information?
Metadata as solution
Metadata as problem
Metadata

Metadata
The obvious solution to the problem is to describe the documents
that is, to attach metadata to the documents
metadata in this context is “information about a document”
So how does this help?
it’s useful for managing the content
it provides a better starting point for search
it means better search results can be displayed
it helps the user determine whether or not a
search hit is interesting
But is it what the user is looking for?
the user starts out wanting to know more about
a subject
traditional metadata, however, focuses on the
document
if aboutness is provided at all, it gets squeezed into a single field
Title: Recurrent Herpes Simplex Sciatica and its Treatment with Amantadine Hydro...
Author: D.A. Fisher
Date: 1982-05
Format: text/html
Keywords: sciatic neuralgia, aman...

What’s wrong with keywords?
The main problem is that their use is uncontrolled
This leads to problems like
authors misspelling keywords,
authors using different keywords for the same thing, and
authors using keywords that make no sense
A secondary problem is that short of guessing, there is no way for the user to find out what keywords have been used
The main benefit is that it’s cheap and simple

Taking control over the vocabulary
The obvious solution is to create a list of legal keywords
this is what’s known as a controlled vocabulary
in a controlled vocabulary keywords are called terms
this requires somewhere to keep the list, and a process for adding new terms
Benefits
gets rid of the misspelling problem
gets rid of the problem with authors using different terms for the same thing
Disadvantages
introduces some overhead
a flat list is difficult to manage
users can still search using the wrong terms
users will still have difficulty finding terms if the list is long
authors will have the same problem

Organizing the terms
The solution is clearly to organize the terms somehow
In one sense we’re now back to the problem we had originally with documents
the solution is also the same: we need to describe the terms somehow
the difficulty is: what can you say about terms?
The good news is that there are many traditional and well-known ways to approach this

Two worlds
amantadine hydrochloride
sciatic neuralgia
Title: Recurrent Herpes Simplex Sciatica and its Treatment with Amantadine Hydro...
Author: D.A. Fisher
Date: 1982-05
Format: text/html
Keywords: sciatic neuralgia, amantadine hydrochloride
?
?
?
Metadata
Subject-based classification

Describing the terms
Tags
Taxonomies
Thesauri
Classification approaches

Subject-based classification
There are many possible organizing principles for documents
By
author
time period
genre
etc
Subject-based classification classifies documents by their subject
the subject is what the document is about
that is, the subject matter of the document
Subject-based classification does not have any particular structure
it's just an approach, and there are many different ways to do it

Folksonomies and tags
Tags have recently become popular on the web
used by web 2.0 sites like Flickr, Technorati, del.icio.us, ...
also much used in blogs to categorize the posts
Tags are effectively a controlled vocabulary of keywords
except the control is often extremely lax
The same benefits and problems
del.icio.us for example has tags like xtm, topic_maps, topicmaps, topic_map, and topicmap

Taxonomies
BT
Organizes the keywords into a tree
the most general at the top, more specific as you go down
common structure used by Yahoo!, LOS, Dewey classification...
Requires relationships between terms
the relationships state that one term is more specific than another
http://www.dmoz.org

A taxonomy example
Nervous system disease
Autonomous nervous system disease
Peripheral nervous system disease
Cauda equina syndrome
Diabethic neuropathy
Sciatic neuralgia

Thesauri
USE
BT
BT
RT
SN
Thesaurus
Taxonomy
Folksonomy
An extension of taxonomies
come from the library world; much used in publishing
the main extension is that thesauri add more relationships
What thesauri contain:
BT the same relationship as in taxonomies
RT related term, which goes across the hierarchy
USE refers to a term that should be used instead of the current one
SN scope note, a definition of the term

A thesaurus example
USE
Peripheral neuropathy
Diabetic complications
Diabetic neuropathy
Sciatic neuralgia
RT

Faceted classification
The term “faceted classification” has been used to mean many different things
originally invented by S. R. Ranganathan in the 1930s
Faceted classification
defines a number of facets or dimensions
defines a set of terms within each facet
sometimes these terms are arranged in a taxonomy
documents are classified against each facet separately

Colon Classification
Ranganathan's original faceted classification system
Consisted of five facets:
Personality The main subject of the document
Matter The material or substance the document deals with
Energy The processes or activities described
Space The location described
Time The time period described
This has sometimes been referred to as “PMEST”

An example of use
The Norwegian wine monopoly describes its products using these facets:
type: red wine, white wine, beer, ...
country of origin: France, Norway, ...
price
matches food: pasta, cheese, fish, beef, ...
bottle size

Ontology in Topic Maps
A Topic Maps model of some specific aspect of the world
Worked on
MODE
Project
Person
CV
Jane Doe
ontology
instances
worked on
CV

Taxonomies and thesauri revisited
From the Topic Maps perspective taxonomies are an ontology
terms become topics (of type “term” or “concept”)
relations become associations (of various types)
scope notes become occurrences
However, in Topic Maps it’s possible to be more precise
USE
Body part
Disease
Diabetic neuropathy
Drug
Amantadine hydrochloride
Sciatic neuralgia
Part of
Attacks
Treats

Expressivity progression
Topic Maps
Taxonomies, thesauri
Flat list, tags
Expressivity
No model
Closed model
Open model

Metadata revisited
Metadata can also be represented in Topic Maps
create topics for the documents
map fields to names, occurrences, or associations
Big pharma
Amantadine hydrochlorine
Sciatic neuralgia
attacks
about
author of
Peripheral nervous system
treated by
D.A. Fisher
This part is untrue!
produced by
works for
Recurrent Herpes Simplex and its...
Date: 1982-05
Format: text/html

Benefits of Topic Maps
Richer, more expressive model
multiple paths to the information you seek
typed associations provide “signposts” along the path
Improved support for search
search for concepts, rather than just documents
associations can be used for filtering
Merges classification and metadata into a single model
greater expressivity (again)
simpler architecture: just one system to relate to
Maps directly to web portals
easy to build and maintain web portal based on the topic map

Conclusion
Traditional findability solutions
metadata: describes documents
classifications: gather and loosely organize keywords/terms
Traditional solutions focus on documents
Users focus on subjects
Topic Maps
open model for describing anything
focus on subjects
easily supports both metadata and existing classifications

What it actually looks like
Deeper into Topic Maps

Advanced concepts
Association roles
Reification
Scope
Identity

Associations have no direction
Puccini
Angeloni
pupil of

Instead associations have roles
Puccini
Angeloni
pupil of
pupil
teacher

Richer relationships
father
child
Lars Marius
Bjørg
Knut
parenthood
mother

Roles, role players and role types
person
pupil
teacher
teacher-of
person
topic type
role type
role type
association type
topic type
association
role player
role player
role
role
puccini
angeloni
N.B. role == association role and role type == association role type

Symmetric relationships
country
neighbor
neighbor
borders-with
country
topic type
role type
role type
association type
topic type
association
role player
role player
role
role
norway
sweden

Reification
From latin “re” = “thing”
i.e. “thingification”
In Topic Maps, for “thing” read “topic”
So reification is about turning something into a topic
Specifically it is about turning topic map constructs that are not already topics (i.e., names, occurrences, associations, association roles, and topic maps) into topic
Useful for annotation of Topic Maps constructs

Reification example
Ontopia
start date
2000
2007
LMG's employment
end date
employed by
Lars Marius Garshol
Obviously, this is no longer the case. But how can we express that?

The semantics of reification
Many possible interpretations of what the reifying topic represents:
the same thing as the association
the association as Topic Maps construct
the assertion of this particular association
Topic Maps reification is case (a)
RDF reification is not formally defined, but is case (c)

Scope
Every statement in a topic map has a scope
that is, a set of topics representing the context in which the statement is valid
the empty set is known as "the unconstrained scope"
Abugida
Alphasyllabary
Bright
Daniels
Tibetan script

Applications of scope
Multilinguality
scope names and occurrences with language topics
Authority
scope statements with the authority that supports them
Provenance
scope statements with their source
Time
scope statements with the era in which they were true

Multiple topics in scope
The context is the intersection of the topics
a statement scoped with "Thursdays" and "LMG" is true on Thursdays according to LMG
Implication: adding topics to the scope narrows the context of validity
given
a statement s in scope a, and
s' in scope a, b
we can see that s' is actually redundant

Topics and subjects
A topic is a representation of a subject
topic: Topic Maps construct representing subject
subject: real-world thing
subject
topic
Patrick Durusau
"subject: anything whatsoever, regardless of whether it exists or has any other specific characteristics, about which anything whatsoever may be asserted by any means whatsoever" --ISO/IEC 13250-2:2006

Subject identification
Topics can have globally unique identifiers attached to them
these identifiers really identify the subject of the topic, and not the topic itself
the identifiers are URIs
However, these are of two different kinds...

Subject locators
A subject locator is a URI that points to the information resource which is the subject
Patrick Durusau
depicted-in
Photo of Patrick
taken-at
Leipzig
http://larsga.geirove.org/photoserv.fcgi?t121182
subject locator
http://larsga.geirove.org/photoserv.fcgi?t121182
same as URI of photo

Subject identifiers
A subject identifier is a URI which refers to an information resource describing the subject
Patrick Durusau
depicted-in
http://psi.ontopedia.net/Patrick_Durusau
Photo of Patrick

Merging
In Topic Maps, two topics must be merged if they have the same
subject identifier,
subject locator, or
reified construct
The rationale is that if this is the case they must represent the same subject

Example
Patrick Durusau
depicted-in
Photo of Patrick
taken-at
Leipzig
editor-of
ISO/IEC 13250-5
Patrick Durusau
editor-of
ODF

Example
editor-of
ISO/IEC 13250-5
Patrick Durusau
depicted-in
editor-of
Photo of Patrick
taken-at
ODF
Leipzig

On merging
Merging is not a special operation
happens every time Topic Maps data is loaded
Allows exchange of fragments
identifiers ensure that fragments are reassembled simply by being loaded
Allows reuse of data
define identifiers for vocabulary (pieces of ontology)
or for individual entities

Examples of use
Subclassing
SIs for this are defined in the standard
can be interchanged between tools
Hierarchy definition
SIs for this were defined years ago; widely used today
Schema language
SIs defined in TMCL (about which more later)
Countries and languages
SIs defined by OASIS
...

LOS
A common classification for public information in Norway
published by Norge.no (Norway.no)
http://norge.no/los/
Consists of
a taxonomy of subjects,
a taxonomy of geographic locations, and
a set of classified resources
Defines PSIs for the subjects and locations
Used by
Bergen Kommune

Grep
The Norwegian National Curriculum
basically the official definition of what children should learn in school
published as a topic map by the Ministry of Education
uses PSIs for all elements
Currently starting to be used
NRK project used it
others are connecting to it, too
an aggregator service is being built

Linked Open Data?
This is linked open data
using URIs to automatically connect statements from disparate sources
Represented in different ways
some use RDF
some use Topic Maps
and some, probably, use other things
Called "Global Knowledge Federation" in the TM community
the concept remains the same
interchange across technologies is possible

HyTime
The Davenport project
ISO
A bit of history

HyTime
An ISO standard for hypertext first published as ISO/IEC 10744:1992
very ambitious and complex
based on SGML (precursor of XML)
many kinds of hyperlinks
including links with any number of anchors, where each anchor is associated with a role type specifying its meaning...
contains a metamodel for representing content to allow detailed addressing into any form of resource
...

Small beginnings
1991
The Davenport Group: project to merge back-of-book indexes to UNIX documentation from different publishers
First attempt known as SOFABED (failed)
1993
CApH was set up, to use HyTime to solve the problem
turned SOFABED into Topic Navigation Maps
1996
picked up by ISO committee responsible for SGML

ISO and TopicMaps.Org
1998
Topic Maps standard submitted for final ballot
an SGML architectural form based on HyTime
SGML syntax today known as HyTM
W3C publishes XML
2000
ISO publishes ISO/IEC 13250:2000 (still in SGML)
TopicMaps.Org created to produce an XML version of Topic Maps
2001
XTM 1.0 published by TopicMaps.Org in March

ISO
2001
work begins on data models for Topic Maps
an infoset-based model, close to XTM 1.0
a graph-based model, far more abstract
lots of politics, holding up all other work
first commercial engine released (Ontopia)
2002
ISO publishes ISO/IEC 13250:2002 (with XTM 1.0)
the first Norwegian portals start appearing
2006
ISO publishes ISO/IEC 13250-2:2006 – Topic Maps – Data Model

A little ISO history
Topic Maps Data Model
The Topic Maps Standards

The new ISO 13250
A multi-part standard, consisting of
Part 1: Overview of Basic Concepts
Part 2: Data Model
Part 3: XTM syntax
Part 4: Canonical XTM
Part 5: Reference Model
Part 6: Compact Syntax
Part 7: Graphical Notation

Roadmap to the TM standards
ISO 18048
QUERY LANGUAGETMQL
ISO 13250
XTM SYNTAX
CXTM SPEC
CTM SYNTAX
GTM NOTATION
ISO 19756
CONSTRAINT LANGUAGETMCL
DATA MODELTMDM
REFERENCE MODELTMRM

The Topic Maps Data Model (TMDM)
Created to define meaning and structure of topic maps
Syntaxes map to this structure, as do TMQL and TMCL
Defines the meaning of topic map concepts using prose
Defines “subject”, “topic”, “scope”, “association”, ...
Defines their structure using the information set model
Just like XML Infoset
Describes the kinds of things that exist in topic maps, and their properties
Adds constraints on the model
Rules for allowed values
Also defines when merging happens, and how

How TMDM works
One information item type defined for each topic map construct
Complete list shown below
One set of properties defined for each construct
Example below: all topic map objects have item identifiers

Association
Associations have the following properties:
[type]: topic defining the association type
[scope]: set of topics making up the scope of the association
[roles]: set of association role items
[reifier]: topic reifying the association
[source locators]: URIs pointing back to element(s) the association came from
[parent]: the topic map
Merge if equal values for [type], [scope] & [roles]

Merging rules in TMDM
One merging rule defined for each information type
Equality rule says which properties to compare (as for association)
Merging rule says how to merge two equal information items
For topics, the equality rule is that two topics are equal if
same value in [subject identifiers] property of both, or
same value in [subject locators] property of both, or
same value in [source locators] property of both, or
some extra conditions
Merging topics is done by
creating a new topic item, whose properties contain the union of the old values,
then replacing all occurrences of the old items throughout the model with the new one

XTM 2.0 syntax
<topicMap version="2.0" xmlns="http://www.topicmaps.org/xtm/">
<topic id="xtm">
<subjectIdentifier href="http://psi.example.org/xtm/2.0"/>
<instanceOf>
<topicRef href="#syntax"/>
</instanceOf>
<name>
<value>XTM 2.0</value>
</name>
<occurrence>
<type>
<topicRef href="#status"/>
</type>
<resourceData>International Standard</resourceData>
</occurrence>
</topic>
</topicMap>

CTM syntax
http://psi.example.org/xtm/2.0 isa syntax;
- "XTM 2.0";
status: "International Standard".

TMCL example
op:Image isa tmcl:topic-type;
is-abstract();
has-name(tmdm:topic-name, 1, 1);
has-occurrence(ph:time-taken, 1, 1);
plays-role(op:Image, ph:taken-at, 1, 1);
plays-role(op:Image, ph:taken-during, 0, 1);
plays-role(ph:depiction, ph:depicted-in, 0, *);
# ...

Topic Maps and RDF

Things
A thing in the real world
S
A symbol in the computer domain
The heart of RDF and Topic Maps is the same:
symbols representing real-world things
Both RDF and Topic Maps consist of statements about these things

Technical comparison
Topic Maps and RDF
are graph-based data models,
have well-defined identity tests and merging operators,
have XML-based interchange syntaxes (as well as human-friendly ones),
are standards, and
have standardized schema and query languages
Differences
RDF is lower-level than Topic Maps,
Topic Maps support reification, complex context, and n-ary relationships, and
Topic Maps distinguish different kinds of URI references

Topic Maps vs RDF
OWL
TMQL
TMCL
SPARQL
RDFS
Topic Maps
RDF
XTM
CTM
RDF/XML
n3

Timeline
MCF-XML
RDF Schema
PICS-NG
MCF
RDF WD
OWL
RDF Rec
'91
'92
'93
'94
'95
'96
'97
'98
'99
'00
'01
'02
'03
'04
ISO work starts
XTM to ISO
Standard finished
ISO 13250:2003
SOFABED model
ISO 13250:2000
XTM 1.0
Davenport Group
TopicMaps.Org
Topic navigation maps

Assertions
RDF has one kind of assertion: the statement
subject, predicate, object
Topic maps have three kinds
(1) Names
(2) Occurrences
(3) Associations
“...”
“...”
http://www...

Handling of identity
Topic Maps
subject locator
subject identifier
item identifier
RDF
uri
blank node
The distinction between a URI referring to a description
of the subject, and a URI referring to the subject cannot
be expressed in RDF.

TMCL vs RDFS/OWL
TMCL
schema language
validation semantics only
very little reasoning or logic
designed to support validation and introspection
RDFS/OWL
ontology description languages
reasoning semantics only
strong basis in logic
OWL is essentially Description Logic

Semantic Portals
eLearning
Business Process Modelling
Product Configuration
Information Integration
Metadata Management
Business Rules Management
IT Asset Management
Asset Management (Manufacturing)
...
Applications of Topic Maps

forskning.no
Norwegian government portal to popular science and research information
basically an online popular science journal
owned by the Norwegian Research Council
Purpose:
To present science and research
information to young adults
Intended to raise interest and
recruitment

Content of forskning.no
The main content is articles about science and research subjects
There is also a classification system used as a navigational structure
The site is entirely topic map-driven
Navigation structure is a topic map
Articles are represented as topics
Even images are topics...

Medicine
Science
Odontology
Human body
Volcanoes
Clinical Med.
Hormones
The Brain
Neurology
Oncology
The Dual Classification

The subject
Subjects
Fields
People
Articles
A Subject

Article
Subjects
Fields
Next article
People
An Article

Person
Title
Home page
Mentioned in
Employer
A Person

The Project
Wide ontology; research covers everything
Ontology was created by reusing an existing thesaurus, automatically converted
A series of 4-5 workshops established the basic principles
Finally, the publishing application was built by Bouvet
software used is ZTM (Python-based, open source)

Maintenance
Maintained by central editorial staff in Oslo
Articles written by distributed network of authors
Authors write and submit articles online
Articles enter workflow and are added by editors
Editors also add connections to topic map

forskning.no admin interface

forskning.no admin interface, 2

forskning.no admin interface, 3

City of Bergen
Second biggest city in Norway
250,000 inhabitants and 20,000 employees
spends roughly 3 million USD annually on the portal project
goal: to make all city services available through the portal
Strong technology platform
Oracle Portal + Oracle RDBMS
Escenic as CMS
Ontopia as Topic Maps engine
DB2TM for data integration

Bergen: who does what?
Most of the site is produced by Ontopia
Some parts by Escenic
Some are independent
And some are service-specific portlets
Static
Escenic

Bergen architecture
Service Catalog
Oracle Portal
Fellesdata
Ontopia
Dexter
DB2TM
TMSync
Agresso
Escenic
Ontopoly
LOS
Editors

NRK/Skole
Norwegian National Broadcasting (NRK)
media resources from the archives
published for use in schools
integrated with the National Curriculum
In production
opened late 2008
Technologies
Ontopia
DB2TM conversion
MySQL database
Tomcat application server

Curriculum-based browsing (1)
Curriculum
Social studies
High school

Gender roles

One video (prime minister’s husband)
Metadata
Subject
Person
Related
clips
Description

GREP
Norwegian national curriculum
published as a topic map
has global IDs on all topics
NRK/Skole clips attached to knowledge goals
global IDs are in the topic map
Therefore...
Grade
Subject
Section
Goal
GREP
Clip
NRK/Skole

ndla.no
Portal organizing learning resources into the curriculum
to be integrated with NRK/Skole

Hafslund
ERP
Billing
Archive
...
SDshare
SDshare
SDshare
SDshare
Topic Map
auto-tagging

SDshare
ERP
SDshare
Server
Client
Fragments

Using Ontopia
DB2TM converts to Topic Maps
a simple XML mapping file
this is enough to provide full sync
Generic SDshare implementation
listens for change events
produces corresponding feeds
ERP
DB2TM
Ontopia
SDshare
Server

Hafslund – points to note
Extremely loose coupling
ontology can be freely changed
Very simple integration
in many cases just an XML configuration file
Very flexible architecture
adding new sources is trivial
Has more uses than just archiving
once the data is collected...

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 haveno idea what a topic map is…
They capture the key concepts, name them, describe them, and associate them with others
This helps them
Capture the essence,
Describe what they have learned,
Keep track of their knowledge, and
Lets the teacher help them
BrainBank was built using Ontopia
An application of the Web Editor Framework
Demonstrates user-friendliness of TM editing

Business process modelling
A multinational petrochemical company uses Ontopia for 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
Processes can be related through
Composition (one process is part of another),
Sequencing (one process is followed by another),
Specialization (one process is a special caseof a more general process)

Product configuration
A Scandinavian telecom company uses Ontopia to manage product configuration
Products belong to families
Features belong to either products or product families
Features are grouped in feature sets
There are dependencies between features
etc.
The system models dependencies in
a topic map
Product configuration engineers use this to configure products using a user-friendly interface
After each change, interface gives feedback on whether selection was valid
Features
Product
families
Versioning
System
data
Products

Product configuration (2)
Feature 1
The features are arranged in a tree
trees vary in size (700-2500 features)
two kinds of parent-child relationships (mandatory or optional)
Configuration rules run across
three different kinds of rules
expressed as associations
In addition: variables
these have different values for different products
Feature 2
conflicts-with
requires
Feature 3
Feature 4
Feature 5

Product configuration (3)
The network of dependencies is already quite complex
Now throw versioning into the mix!
Managing all this data is not easy
The system is driven by inference rules
These work on the topic map
Easily capture complex logic
Also integrates with product documentation
Very complex topic map
at the last count ~20,000 topics and ~1,000,000 associations
running complex queries on this really exercises the query engine

Business rules management (1)
The US Department of Energy has used Ontopia to manage guidance rules for security classification
Information about the production of nuclear weapons is subject to thousands of rules
Rules are published in 100s of documents
Most documents are derived from more general documents

Business rules management (2)
Guidance topics form a complex web of relationships that is captured in a topic map
Concepts are connected to if-then-else rules
This constitutes a knowledge base (KB)
KB used with an inference engine to automatically
classify information (documents, emails, ...), and
redact information (PDF, email, ...)
Benefits:
Model expressive enough to capture thecomplexity of the rules
Status as ISO standard ensures stability and longevity
Master
topic
Parent
topic
Child
topic
Guidance
topic
Derived
topic
Responsible
person
Concept
Workflow
state

IT asset management
The University of Oslo is using the OKS to manage IT assets
Servers, clusters, databases, etc are 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
Two applications provide accessto the knowledge base
Whitney: online
Houson: offline (for use in emergencies)
Houdini
Whitney
Syntax control
OKS schema validation
Versioning with CVS
Navigator framework
UIOTM FW
OKS API
OKS Engine
RDBMS backend
XTM
usit.ltm(handcoded)
oracle.ltm(generated)
CVS

Asset management: Manufacturing
The Y-12 plant at DoE is using the OKS to map its plant
The purpose is to get an overview of
equipment,
processes,
materials required,
parts already built,
etc.

Topic Maps software

Two main kinds
Big application suites
complete frameworks for building solutions
engines at the core with end-user tools on top
Smaller, open source tools
many are just engines
some are more specific tools for a single purpose

Ontopia
Open source Java-based suite of tools
engine + query engine
generic ontology designer + instance editor
conversion tools (RDBMS, RDF, XML, ...)
presentation frameworks (JSP, portlets, ...)
CMS integrations
automatic classification
graphical visualization
web service interfaces
browser
...

Web3
Commercial .NET-based suite
engine + query engine
Sharepoint integration
built-in security model
web service interfaces
presentation framework

Topincs
Web-based knowledge management tool
wiki-like, but TMCL-based
collaborative
complex presentation features
version 5.1 allows embedded programming in the TM

Wandora
Open source Java-based application suite
core engine
extensive set of input converters
many export formats
ontology designer + instance editor

topicWorks
Commercial Java-based application suite
core engine
sophisticated data navigator
Excel plugin
ready-made ontologies

ZTM
Open source Topic Maps-based CMS
written in Python, on top of Zope
used for a large number of portals (e.g vestforsk.no)
very advanced CMS features
enables very rapid development

Atom2
Commercial suite
high-performance engine + query engine
ontology designer + instance editor
CMS-like functionality

TopicMapsLab
SesameTM
TMAPI implementation on top of Sesame triple store
tmql4j
TMQL query engine on top of TMAPI
Aranuka
object mapping library
Onotoa
graphical TMCL modelling tool
Maiana
social Topic Maps browser
MajorTom
virtual merging Topic Maps engine
...

Various engines
TM++ C++
tmjs JavaScript
QuaaxTM PHP
Mappa Python
RTM Ruby
SharpTM C#
TM2JDBC Java
Isidorus Common Lisp
tinyTiM Java
...

Sources
Learn more

Papers
Topic Maps in Encyclopedia of Library Science
http://www.ontopedia.net/pepper/papers/ELIS-TopicMaps.pdf
The TAO of Topic Maps
http://www.ontopia.net/topicmaps/materials/tao.html
Metadata? Thesauri? Taxonomies? Topic Maps!
http://www.ontopia.net/topicmaps/materials/tm-vs-thesauri.html

Conferences
Software 2011 – Topic Maps track
http://www.dataforeningen.no/forside.168724..html
TMRA conferences
http://tmra.de

Other
Topic Maps Snippets
http://topicmaps.bouvet.no/blog/
Planet Topic Maps
http://planet.topicmaps.org/
TopicMaps.org
http://www.topicmaps.org
TopicMaps Lab
http://www.topicmapslab.de
Index of Topic Maps software
http://www.garshol.priv.no/tmtools/

http://www.dataforeningen.no	39
http://www.techgig.com	9
http://sp2010.posterous.com	7
http://www.topicmapslab.de	2
https://www.dataforeningen.no	1
http://posterous.com	1

Topic Maps - Human-oriented semantics?

by Lars Marius Garshol

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