Introduction to Topic Maps and Subjectcentric Computing - PowerPoint PPT Presentation


PPT – Introduction to Topic Maps and Subjectcentric Computing PowerPoint presentation | free to view - id: 20b2d7-ZDc1Z


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation

Introduction to Topic Maps and Subjectcentric Computing


Basic Concepts (TAO of Topic Maps) Advanced Concepts (scope and roles) ... bio, map, synopsis) knowledge layer. information layer. Puccini. Tosca. Lucca. composed by ... – PowerPoint PPT presentation

Number of Views:135
Avg rating:3.0/5.0
Slides: 111
Provided by: httppsiont


Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Introduction to Topic Maps and Subjectcentric Computing

Introduction to Topic Maps and Subject-centric
  • Bors István
  • Budapest, 2009-07-21

  • Basic Concepts (TAO of Topic Maps)
  • Advanced Concepts (scope and roles)
  • Writing a Simple Topic Map in LTM
  • Ontology-driven Editing with Ontopoly
  • Breaks
  • 10.30 11.00 (coffee)
  • 12.30 13.30 (lunch)
  • 15.00 15.30 (coffee)

The copernican revolution
  • For 1,000s of years people thought that the sun
    revolved around the earth
  • Actually some Greek, Indian and Muslim scholars
    knew better, but the view of Aristotle, Ptolemy
    and the Christian Church was dominant
  • The publication of On the revolutions of the
    celestial spheres (1543) by Nicolaus Copernicus
    changed all that
  • The heliocentric theory turned our understanding
    of the universe upside-down or inside out.

Computing has a similar problem
  • Today we face a similar situation in computing
    and information management
  • Our computing universe has applications (and
    documents) at the centre
  • This is wrong, because it does not reflect how
    humans think
  • Humans think in terms of subjects (or concepts)

The subject-centric revolution
  • We must put subjects at the centre, because
    thats what really interests us
  • For example, when looking for information
  • This is the subject-centric approach
  • It represents a radically different way of
    organizing information and knowledge
  • Subject-centric computing is what Topic Maps is
    really all about

What is Topic Maps?
  • An ISO standard for computer-based
    information and knowledge management
  • Provides the ability to control infoglut and
    share knowledge by connecting any kind of
    information from any kind of source based on its
  • A semantic technology
  • Cf. Semantic Web (RDF, OWL)
  • A form of knowledge representation
  • Widely used for web-based delivery of information
  • Plus Information Integration, eLearning,
    Business Process Modeling, Product Configuration,
    Business Rules Management, Asset Management,
    Knowledge Management, …

Background to Topic Maps
  • Emerged from the SGML community in 1990s
  • Initial use case How to merge (digital)
    back-of-book indexes
  • Some input from library science
  • Precious little input from computer scientists
    before 2001
  • Most of the SGML community came from the
  • ISO 13250 first published in 2000 (recently
  • A model for representing knowledge organization
    structures (indexes, glossaries, thesauri,
  • Plus interchange syntax, query language,
    constraint language, ...
  • Widely adopted in Norway (esp. public sector)
  • And gaining ground elsewhere

Basic Concepts The TAO of Topic Maps
  • Topics
  • Associations
  • Occurrences

The TAO of Topic Maps
Callas, Maria …………………… 42 Cavalleria Rusticana …
71, 203-204 Mascagni, Pietro Cavalleria
Rusticana . 71, 203-204 Pavarotti, Luciano ………………
45 Puccini, Giacomo ………. 23, 26-31 Tosca
………………. 65, 201-202 Rustic Chivalry, see
Cavalleria Rusticana singers ……………………….
39-52 baritone ………………………. 46 bass
……………………….. 46-47 soprano ……………… 41-42, 337
tenor ………………………. 44-45 see also Callas,
Pavarotti Tosca ………………… 65, 201-202
  • The core concepts are derived from the
    back-of-book index
  • Extended and generalized for use with digital
  • Consider a two-layer model consisting of
  • a set of information resources (below)
  • a knowledge map (above)
  • This is like the division of a book into content
    and index

knowledge layer
information layer
(1) The information layer
  • The lower layer contains the content
  • usually digital, but need not be
  • can be in any format or notation or location
  • can be text, graphics, video, audio whatever
  • This is like the content of the book to which
    the back-of-book index belongs

information layer
(2) The knowledge layer
  • The upper layer consists of (typed) topics and
  • Topics represent the subjects that the
    information is about
  • Like the list of topics that forms a back-of-book
  • Associations represent relationships between
    those subjects
  • Like see also relationships in a back-of-book

composed by
Domain Italian opera
composed by
Madame Butterfly
born in
knowledge layer
Occurrences link the layers
  • Occurrences represent relationships between
    information resources and the subjects that they
    are about
  • The links (or locators) are like page numbers in
    a back-of-book index
  • Occurrences can also be typed (e.g. bio, map,

Summary of core concepts
Lets look at some TAOs in the Omnigator…
  • The TAO of Topic Maps

Plus topic types, association types, occurrence
types each of which are represented by topics...
Omnigator interface
The power of the TAO model (1)
  • Represent subjects explicitly
  • Topics represent the things your users are
    interested in
  • Capture relationships between subjects
  • Associations provide user-friendly navigation
    paths to information (navigation as we may
  • Associations promote serendipitous knowledge
    discovery through browsing
  • Make information findable
  • Topics provide a one-stop-shop for everything
    that is known about a subject (collocation of
    information and knowledge)
  • Occurrences allow information about a common
    subject to be linked across multiple systems

The power of the TAO model (2)
  • Represent taxonomies and thesauri
  • Associations may represent hierarchical
  • Topic Maps permits multiple, interlinked
    hierarchies and faceted classification
  • Transcend simple hierarchies
  • Rich associative structures capture the
    complexity of knowledge and reflect the way
    people think
  • Manage knowledge
  • The topic map is the embodiment of corporate
  • It provides a structured way to capture peoples
    knowledge of things, events, relationships, etc.

Querying topic maps
  • Topic Maps is based on a formal data model
  • This means that topic maps can be queried, like
  • Topic Maps Query Language (TMQL)
  • Allows more powerful use of taxonomies to
    retrieve information
  • Permits queries that would make Google boggle
    (see below)
  • Based on Ontopias query language tolog
  • (Demo of querying in the Omnigator)
  • Query example
  • Give me all composers that composed operas that
    were based on plays that were written by

Advanced Concepts Scope and Roles
The problem of context
  • A topic map captures knowledge, but...
  • Some knowledge is only valid in a certain context
  • Reality is ambiguous
  • Knowledge has a subjective dimension
  • People have different opinions
  • Context is handled using scope
  • Enables the expression of contextual validity
  • Allows the expression of multiple world views

How scope works
  • We make statements about topics
  • names, occurrences, associations
  • Every statement is valid within some context
  • Statements are qualified by scope
  • the name Allemagne for the topic Germany in the
    scope French
  • a certain information occurrence in the scope
  • a given association is true in the scope
    (according to) Authority X

Topics play roles in associations
  • Associations have no direction
  • They represent relationships and are inherently
  • Puccini was born in Lucca
  • Lucca was the birthplace of Puccini
  • Two ways to express the same relationship
  • Impression of direction caused by use of natural
  • One of the topics viewed as the subject and the
    other as the object
  • Instead of direction, associations use roles
  • Puccini plays the role of person and Lucca plays
    the role of place
  • person and place are association role types (or
    role types, for short)
  • Labels are assigned based on role perspective

Anatomy of an association
  • Role types characterize the nature of the
    subjects involvement in the relationship
  • They are also topics

Associations need not be binary
  • Unary associations are not common
  • Useful for representing properties that have
    boolean values
  • e.g., the property of being unfinished
  • Binary associations are the most common
  • Often correspond to verb ( subject, object )
  • Ternary associations are quite common
  • Often correspond to verb( subject, direct-object,
    indirect-object ) constructs
  • N-ary associations (where n gt 3)
  • Less common but sometimes useful
  • Many n-ary associations are better represented as
    (n-1) binary associations...

The Topic Maps standards
  • ISO/IEC 13250 Topic Maps
  • Part 1 Overview and Basic Concepts
  • Part 2 Data Model
  • Part 3 XML Syntax
  • Part 4 Canonicalization
  • Part 5 Reference Model
  • Part 6 Compact Syntax
  • Part 7 Graphical Notation
  • ISO/IEC 18048
  • Topic Maps Query Language
  • ISO/IEC 19756
  • Topic Maps Constraint Language
  • ISO/IEC TR 29111
  • Expressing Dublin Core Metadata Using Topic Maps

Creating a topic map Interchange syntaxes
  • HyTM, XTM, LTM and CTM
  • Using LTM

Interchange syntaxes
  • HyTM (HyTime Topic Maps)
  • Original syntax, expressed in terms of SGML and
  • No longer part of ISO 13250
  • XTM (XML Topic Maps Syntax)
  • Later, XML-based syntax, recently moved to
    version 2.0
  • Easy to understand but very verbose
  • LTM (Linear Topic Map Notation)
  • Defined by Ontopia in 2001 and supported by other
  • A simple ASCII syntax for rapid prototyping
  • CTM (Compact Topic Maps Syntax)
  • ISO standard replacement for LTM
  • Complete draft exists, but not yet finalized

XTM 1.0 Syntax example
lttopic id"la-boheme"gt ltinstanceOfgtlttopicRef
xlinkhref"opera"/gtlt/instanceOfgt ltbaseNamegt
ltbaseNameStringgtLa Bohèmelt/baseNameStringgt
ltvariantgt ltparametersgt
ltsubjectIndicatorRef xlinkhref"http//"/gt
lt/parametersgt ltvariantNamegtltresourceDatagtBoh
emelt/resourceDatagtlt/variantNamegt lt/variantgt
lt/baseNamegt ltoccurrencegt ltinstanceOfgtlttopicR
ef xlinkhref"homepage"/gtlt/instanceOfgt
ltresourceRef xlinkhref"http//www.opera.i
lt/occurrencegt lt/topicgt
LTM Syntax example
la-boheme opera "La Bohème" "Boheme"
la-boheme, homepage, "http//
LTM basics
  • Topic topic-id puccini composer
    "Puccini" lucca city "City"
  • Association assoc-type ( player role, player
    role ) born-in ( puccini person, lucca
    place )
  • Occurrence topic-id, occurrence-type, "URL"
    topic-id, occurrence-type, string
    la-boheme, homepage, "http//
    ere/La-Boheme/La-Boheme.html" la-boheme,
    premiere-date, 1896 (1 Feb)
  • Scope (nameoccurrenceassociation) / topic-id

Demo Creating a topic map with LTM
  • A simple knowledge management application to
    capture skills and experience

What the topic map is about
  • People are employed by organizations in certain
  • They have email addresses and other contact
  • They are members of certain professional
    associations and they speak various languages to
    varying degrees.
  • They attend various events (workshops,
    conferences) and write papers.
  • Organizations have web sites and are located in
    certain cities

Some data
  • Bognárné Lovász Katalin
  • 36 305739349
  • University of West Hungary
  • Association of Hungarian School Librarians
  • XI. Summer School for School Librarians
  • School librarian and/or manager?
  • Topic Maps Workshop
  • Hungarian fluent
  • English advanced
  • German basic
  • Fancy dress and tea in the school library(?)
  • Horváthné Szandi Ágnes
  • University of West Hungary
  • http//
  • Szombathely
  • Association of Hungarian School Librarians
  • XI. Summer School for School Librarians
  • http//
  • Summer conference held every second year in
    different locations
  • Association of Hungarian School Librarians
  • Budapest
  • http//

Advanced Concepts Merging and Identity
Merging topic maps
  • Topic Maps can be merged automatically
  • Arbitrary topic maps can be merged into a single
    topic map
  • This cannot be done with databases or XML
  • Merging enables many advanced applications
  • Information integration across repositories
  • Sharing and reusing taxonomies
  • Automated content aggregation
  • Distributed knowledge management

Principles of merging
  • By definition Every topic represents exactly one
  • Our goal Every subject represented by just one
  • When two topic maps are merged, topics that
    represent the same subject should be merged to a
    single topic
  • When two topics are merged, the resulting topic
    has the union of the characteristics of the two
    original topics

Merge the two topics together...
(Demo of merging in the Omnigator…)
Subject identity
  • Precondition for successful merging
  • Knowing when two topics represent the same
  • What makes merging possible?
  • NOT the use of names, which are notoriously
  • Names are not unambiguous (the homonym problem)
  • Many topics have multiple names (the synonym
  • Achievement of the collocation objective
  • Only possible through the use of unique global
  • If subjects have unique identifiers, people are
    free to use whatever names they like, and topic
    maps can still be merged successfully

Subjects and Topics
  • Topics are surrogates, or proxies (inside the
    computer) for the ineffable subjects that you
    want to talk about, such as Puccini, love, these
    slides, or the second law of thermodynamics

The identity of subjects
  • Topics exist in order to allow us to talk about
  • The relationship between the two is sometimes
    called intentionality
  • We need to know exactly which subject a topic
  • That is, we need to establish its subject
  • The collocation objective depends on knowing when
    applications are talking about the same thing

Subject identifiers
  • The identity of most subjects can only be
    established indirectly
  • An information resource can provide an indication
    of the subjects identity to a human
  • Such a resource is called a subject descriptor
  • A subject descriptor has an address, even though
    the subject it indicates does not
  • Computers can use the address of the subject
    descriptor to establish identity
  • Such addresses are called subject identifiers
  • Subject descriptors and subject identifiers are
    the two sides of the human-computer dichotomy

Advice on subject identifiers
  • Always use them for your typing topics
  • Makes your topic map and your ontology more
  • The more serious your application, the more
    extensively you should use them for instances
  • Remember Merging with other topic maps will not
    be successful without identifiers
  • LTM code for subject identifiers
  • See ItalianOpera.ltm
  • Example
  • composer "Composer" _at_"http//psi.ontopedia.n

My conventions for PSIs
  • URI prefix
  • http//
  • Note Not all my identifiers have corresponding
  • URI suffix
  • Initial cap for topic types and role types (e.g.
  • Lower case for association, occurrence and name
    types (e.g. born_in)
  • Wikipedia conventions for instances
  • Replace spaces with underscores
  • Check Norwegian Opera for examples
  • Do not use the Italian Opera Topic Map its
    conventions are outdated

Ontology-driven editing
  • Creating topic maps using Ontopoly

What is an ontology?
  • Shorter Oxford English Dictionary
  • Ontology The science or study of being that
    department of metaphysics which relates to the
    being or essence of things, or to being in the
  • Russell Norvig Artificial Intelligence
  • A particular theory of the nature of being or
  • Tom Gruber
  • A specification of a conceptualization… a
    description of the concepts and relationships
    that can exist for an agent or a community of
  • Wikipedia
  • A data model that represents a set of concepts
    within a domain and the relationships between
    those concepts
  • John Sowa Knowledge Representation
  • A classification of the types and subtypes of
    concepts and relations necessary to describe
    everything in the application domain

Topic Maps terminology
  • Ontology
  • the set of typing topics that is used within a
    given topic map, or that defines a class of topic
  • i.e. the topic types, association types,
    occurrence types, etc.
  • Constraints
  • rules governing classes of objects (i.e. typing
  • Schema
  • the combination of an ontology and constraints
  • Schema language
  • a language for writing schemas
  • e.g. TMCL and OSL (Ontopia Schema Language)

Why you need an ontology
  • An ontology in Topic Maps corresponds to
  • the set of element types and attributes in XML
  • the set of tables and columns in an RDBMS
  • It determines the kinds of things that can exist
    in the topic map
  • In other words, the ontology determines what you
    can say
  • For example
  • You cant express the fact that X and Y are
    organization unless you have a organization
    topic type
  • You cant express the fact that person A is
    employed by organization B unless you have an
    employed by association type
  • etc.

Expressing the ontology
  • The ontology itself is part of the topic map
  • Puccini is a topic of type composer
  • Lucca is a topic of type city
  • composer and city are also topics that are
    present in the same map
  • The association between Puccini and Lucca is of
    type born-in, where Puccini plays the role of
    person and Lucca plays the role of place
  • born-in, person and place are also topics in the
    same map
  • Lucca has an occurrence of type map and Puccini
    an occurrence of type bio
  • map and bio are also topics
  • Etc.

What is ontology-driven editing?
  • A user-friendly way to create topic maps
  • The equivalent of syntax-directed editing in XML
  • The principle is simple
  • The ontology describes what kind of things can
    exist in the topic map
  • It also includes constraints on
  • Which types of statement are used with which
    types of topics
  • What cardinality they have
  • Based on this, the interface is automatically
    configured for data entry
  • The benefits
  • Easier user interface no need to understand
  • More consistent topic maps
  • Ontopoly is such an editor

How to use Ontopoly
  • Read the Ontopoly User Guide!
  • It will save you a lot of grief in the long run
  • Start the program from OKS Samplers / Ontopoly
  • Open an existing Ontopoly topic map
  • Import an existing non-Ontopoly topic map
  • Or create a new topic map
  • Use the Description tab to describe the topic map
  • (Also to validate it and a few other things)
  • Use the Ontology tab to define the ontology
  • topic types, type hierarchy, association types,
    role types, name types, occurrence types
  • fields (names, identifiers, occurrences, and
    associations) that apply to each topic type,
    their order and cardinality
  • Use the Instances tab to populate the data
  • Uses an automatically configured forms-based

Some tips on ontology creation
  • Sketch out the basic ontology on paper first
  • Create the type hierarchy in Ontopoly
  • Keep it simple
  • Create association types and role types
  • Specify what the role-playing topic types are
  • Create occurrence types and name types
  • Go to each topic type in turn, starting at the
    top of each type hierarchy, and assign additional
  • Review the ontology
  • Dont add data until you are fairly comfortable
    with the ontology
  • Later changes to the ontology that invalidate the
    data may cause extra work

Some comments on Ontopoly
  • Does not (yet) support scope or variant names
  • Use typed names instead of scoped names
  • Includes system information in the topic map
  • The topic map can be exported without this
  • It can be hidden in the Omnigator
  • Customize ? Nontopoly model
  • Important points to remember
  • Clicking on any link submits the HTML form, but
    does not save to disk
  • You MUST click on the Save button regularly
  • Changing the ontology when you have already
    entered data can lead to invalid data

Demo Creating a topic map with Ontopoly
  • A simple knowledge management application to
    capture skills and experience

Making information findable
  • Intuitive navigational interfaces for humans
  • The topic/association layer mirrors the way
    people think, learn and remember
  • Powerful semantic queries for applications
  • A formal underlying data structure
  • Customized views based on individual needs
  • Personalized information delivery using scope
  • Information aggregation across systems and
  • Topic Maps can be merged automatically
  • But there is more to Topic Maps than that...

  • Today our desktops are application-centric and
  • Icons represent applications and documents

  • Why cant they be subject-centric, with icons
    that represent the subjects we are interested in?
  • With links between related icons?
  • And with context menus that allow us to find
    everything related to a particular subject?

topic maps
LING 2110
INF 2820
bantu semantics
References (1/2)
  • Articles
  • The TAO of Topic Maps http//
  • ELIS article on Topic Maps http//www.ontopedia.n
  • ISO standards
  • http//
  • Conferences
  • International Topic Maps Users Conference
    (Oslo) http//
  • Topic Maps Research and Applications
    (Leipzig) http//

References (2/2)
  • Mailing lists
  • http//
  • http//
  • Tools
  • Overview of tools http//
  • Ontopia (Open Source Java engine)
  • Blogs, websites, etc.
  • http//
  • http//

(No Transcript)
Topic Maps and RDF
  • Similarities
  • Differences
  • Interoperability

Semantic Web Layer Cake
Two households, both alike in dignity
  • During the late 1990s the W3C and ISO developed
    two semantic technologies in parallel
  • Two communities, largely unaware of each other
  • Tackling the same fundamental problems
  • Findability
  • Semantic interoperability
  • The results were RDF and Topic Maps

How the two families stack up
RDF Schema
Topic Maps
ISO Topic Maps
W3C Semantic Web
Striking similarities
  • Both extend XML into the realm of semantics
  • Both allow assertions to be made about things in
    the real world
  • Both define abstract, associative (graph-based)
  • Both have URI-based models of identity
  • Both allow forms of inferencing or reasoning
  • Both have XML-based interchange syntaxes
  • Both have constraint languages and query
  • But they are also different in some crucial

Important differences
  • Different roots
  • Topic Maps has its roots in traditional finding
    aids (indexes, thesauri, etc.)
  • RDF has its roots in document metadata and formal
  • Different levels of semantics…
  • RDF is more low level Topic Maps has more
    higher-level semantics
  • Different models
  • Identity, scope, association roles, n-ary
    relationships, variant names, …
  • Different goals
  • RDF An artificially intelligent web for software
  • Topic Maps Findability and knowledge integration
    for humans

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

Who can tell me what this is?
  • Is it an H or an A?
  • (Human or Agent)
  • The point is that fuzziness is a fact.
  • Humans can handle it machines cant.

Different capabilities
  • RDF/OWL, to support logic-based inferencing,
    cannot allow fuzziness
  • Topic Maps, because it is for humans, has to
    support fuzziness
  • OWL ontologies tend to be very stringent and
  • Topic Maps ontologies tend to be simpler and less
  • OWL has properties for things that Topic Maps
    doesnt need
  • Topic Maps has features that would be too complex
    for OWL
  • So you need to decide what it is you really need…

RDF or Topic Maps?
  • RDF is more low-level oriented towards machines
  • Topic Maps is more high-level oriented towards
  • OWL is oriented towards artificial intelligence
  • Do you simply want to encode document metadata?
  • RDF is ideal and you wont need OWL
  • Do you want to achieve subject-based
    classification of content?
  • Topic Maps provides the best combination of
    flexibility and user-friendliness
  • Do you want both metadata and subject-based
  • 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,
    youre half way there
  • Whatever you choose, you can always move your
    data between Topic Maps and RDF, thanks to RDFTM…

  • RDF/Topic Maps Interoperability Task Force
  • A task force within the Semantic Web Best
    Practices and Deployment Working Group
  • Chartered to deliver two documents
  • Survey of Existing Interoperability Proposals
  • Guidlines for RDF/Topic Maps Interoperability
  • Survey published in February 2006
  • http//
  • Draft guidelines published in June 2006
  • http//
  • The task force is now disbanded and the work will
    be finalized by SC34

(No Transcript)
Applications of Topic Maps
  • Taxonomy Management
  • Metadata Management
  • Semantic Portals
  • Information Integration
  • eLearning
  • Business Process Modelling
  • Product Configuration
  • Business Rules Management
  • IT Asset Management
  • Asset Management (Manufacturing)

Taxonomy management
  • For managing unstructured content
  • Organization by subject because thats how
    users search
  • A taxonomy is a simple form of topic map
  • Topic Maps provides subject-based organization
  • Using Topic Maps offers many benefits
  • Standards-based means vendor independence and
    data longevity
  • Associative model allows for evolution beyond
    simple hierarchies
  • The taxonomy can also be used as a thesaurus, a
    glossary or an index
  • Identity model permits merging and reuse
  • Dutch Tax and Customs Administration
    (Belastingdienst) uses Topic Maps as the basis of
    a taxonomy management system
  • http//
  • Capability can be added to any Content Management

Metadata management
  • A Metadata Server based on Topic Maps
  • Management of metadata for government
  • Used in the central public information portal
  • Primary goal
  • Ensure much greater consistency in the use of
    metadata across different government publications
    in order to improve findability for users
  • ODIN now re-architected as
  • Solution based on Topic Maps

Semantic portals
  • Topic Maps as the Information Architecture
  • for web-based publishing (web sites, portals,
    intranets, etc.)
  • Site structure is defined as a topic map
  • Each page represents a topic (subject-centric)
  • User-friendly navigation paths defined by
  • Topics used to classify content
  • Potential for subject-based portal connectivity
  • Smooth evolution into Knowledge Management

Enterprise information integration
  • Topic Maps are designed for ease of merging
  • Generate topic maps from structured data (or
    create topic map views of that data)
  • Merge topic maps to provide a unified view of the
  • Easy to filter
  • Create personalized views of this unified model
  • Advantages
  • Consolidated access to all related information
  • No need to migrate existing content
  • Standards-based

Enterprise information integration
  • Example Elmer project at Starbase (Borland)
  • Integration server for software information
  • Multiple disparate applications hold related data
  • Unified topic map layer enables search across
  • Data integration without changing the underlying
  • Portal interface
  • Intuitive navigation
  • Full-text and structured queries
  • Smarttags integration
  • Elmer terms (topic names) highlighted
  • Provide links into the portal

E-learning BrainBank
  • Topic maps are associative knowledge structures
  • They reflect how people acquire and retain
  • Students describe what they have learned
  • Pilot users 11-13 year olds
  • Key learning concepts are
  • captured, named, described
  • associated with other concepts
  • Students are able to
  • capture the essence of a subject
  • describe what they have learned
  • keep track of their knowledge
  • Teachers are able to
  • monitor students understanding

Business processes
  • Multinational petrochemical company
  • Uses TMs to manage business process models
  • Flexible model allows arbitrary relationships to
    be captured easily
  • Processes are modelled in terms of
  • Steps involved, their preconditions, their
    successors, etc
  • Processes 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
  • Managing product configuration for mobile phones
  • Products belong to families
  • Features belong to products or product families
    and are grouped in feature sets
  • There are dependencies between features and they
    apply in different regions, etc.
  • Network of dependencies is already quite complex
  • Now throw versioning into the mix!
  • 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
  • System is driven by inference rules
  • These work on the topic map
  • Easily capture complex logic
  • Also integrates with product documentation

Business rules
  • US Department of Energy Rules for security
  • Information about the production of nuclear
    weapons subject to thousands of rules
  • Rules published in 100s of documents
  • Most documents are derived from more general
  • Guidance topics form a complex web of
  • Captured in a topic map (KB)
  • Concepts connected to if-then-else rules
  • KB used with inference engine
  • automatically classifies information (documents,
    emails, ...), and
  • "redacts" information (PDF, email, ...)
  • Benefits
  • Model expressive enough to capture complexity of
    the rules
  • ISO standard stability longevity

IT assets
  • University of Oslo Management of IT assets
  • Servers, clusters, databases, etc. described in a
    TM (KB)
  • Used to answer questions like
  • If operating system Z is upgraded, what apps are
  • Service X is down, who do I call?
  • If I take Y down, what else goes?
  • Uses composite topic map
  • Partly autogenerated
  • Partly handcoded
  • Two applications
  • Whitney online
  • Houston offline (for use in emergencies)

Manufacturing assets
  • US Department of Energy
  • Topic map describes Y-12 manufacturing facility
  • Provides overview of
  • equipment,
  • processes,
  • materials required,
  • parts already built,
  • etc.

Tools (http//
  • ATop
  • CmapTools
  • ctm-mode
  • dtddoc
  • Escenic Topic Maps module
  • Knowledge Concierge
  • ltm-mode
  • mappa
  • OfficeNet Knowledge Portal
  • Ontopia
  • Perl TM
  • QuaaxTM
  • Ruby Topic Maps
  • ThinkGraph
  • tinyTiM
  • TM/XMLtoXTM1 Converter
  • TM
  • TM4J
  • TM4JScript
  • TM4L
  • TM4Web
  • TMCore
  • TMCore EPiServer Module
  • TMCore Sharepoint Module
  • TMCore Sitecore Module
  • tmedit
  • TMNav
  • TMTab
  • Topincs
  • Wandora
  • Wordpress Topic Maps
  • xSiteable
  • XTM1toXTM2 Converter
  • xtm2xhtml
  • xtm4xmldb
  • ZTM

(No Transcript)
Topic types, type hierarchies and other
Topic types
  • A topic type defines a class of things
  • Its a particular kind of category that has
  • You can also think of it as a set of things that
    have one or more properties in common
  • Rule 1 If it doesnt have instances, it isnt a
  • Music is a category, but not a type (there are
    no instances)
  • nothing is a music
  • Opera is a type, because there are things which
    are operas
  • Tosca is an opera
  • A diagnostic for deciding if foo is a type
  • If you can think of things which are foos the
    answer is yes
  • But be careful Is wine a type?
  • If the answer is no, ask what kind of thing foo
  • Now, that really is a type!

ISA and type-instance
  • The relationship between a type and its instance
    is actually a special kind of association
  • We call it (guess what) a type-instance
  • Its also often called an ISA relationship
  • It can be represented as an association in XTM or
  • But theres no real point
  • Use the syntactic shortcut instead
  • tosca opera

is a
Rules of thumb for topic types
  • Choose an appropriate level of generality
  • Countries is better than Countries in
    South-East Asia
  • The domain of the topic map tells you which
    countries it includes
  • If it doesnt, an association would be a better
  • located-in(Thailand, South-East_Asia)
  • But dont make it so general as to be useless
  • Places instead of countries would mix
    countries and cities
  • Keep the name short
  • That makes it easier to display
  • Use the singular form
  • Experience shows this to be most useful, so
    Country, not Countries
  • Use initial capitals
  • A matter of taste, but I think it looks most tidy

Type hierarchies
  • Some topic types can be arranged in hierarchies
  • Type hierarchies are a natural way to order parts
    of the world
  • Humans are quite familiar with tree structures
  • Type hierarchies provide
  • more user-friendly navigation
  • more powerful querying/inferencing
  • more compact schemas and ontologies
  • greater clarity about the relationships between
  • Use hierarchies, but beware of two pitfalls
  • Not all hierarchies are type hierarchies...
  • Its easy to confuse your ISAs and your AKOs…

Type hierarchies AKO
a dog is A Kind Of canine, a canine is A Kind Of
mammal, etc.
Dragon 1 Mixing ISAs and AKOs
  • Steve is a homo sapiens
  • A homo sapiens is a mammal
  • Therefore Steve is a mammal
  • Steve is a homo sapiens
  • Homo sapiens is a species
  • Therefore Steve is a species

Types, subtypes and instances
How type hierarchies work
  • The superclass-subclass relationship has defined
  • Therefore make sure you use it correctly
  • Software (tolog, for example) will assume you
    mean what you say
  • If you abuse the semantics you will get incorrect
  • If A is a superclass of B, then
  • Both A and B must be classes
  • If C is an instance of B, it must also be an
    instance of A
  • If C is a subclass of B, it must also be a
    subclass of A, (in which case an instance of C
    is also an instance of B and an instance of A)
  • If in doubt define your own association type
  • merging it with superclass/subclass later is

Being both type and instance
  • Most modelling paradigms distinguish between
    type and instance
  • In most paradigms something cannot be both
  • In Topic Maps something can be both type and
  • (or class/category and individual)
  • For example, homo sapiens can be both
  • a type (supertypeprimate, instanceSteve), and
  • an instance (typespecies)
  • So be careful!

Representing a type hierarchy
  • Use associations between typing topics
  • subtypeOf(homo_sapiens subtype, primate
  • subtypeOf(primate subtype, mammal supertype)
  • XTM 1.0 defined identifiers for these three
  • subtypeOf (or superclass-subclass) http//
  • supertype (or superclass) http//www.topicmaps.or
  • subtype (or subclass) http//
  • Topic Maps software understands these and
    implements the semantics for you

Type hierarchies in LTM
  • / Techquila hierarchy PSIs /
  • hierarchical-relation-type "Hierarchical
    relation type"
  • _at_"http//
  • superordinate-role-type "Superordinate role
  • _at_"http//
  • subordinate-role-type "Subordinate role type"
  • _at_"http//
  • / XTM superclass-subclass PSIs /
  • subtypeOf hierarchical-relation-type
  • "Subtype of" "Supertype of" / supertype
  • _at_"http//
  • subtype subordinate-role-type "Subtype"
  • _at_"http//
  • supertype superordinate-role-type
  • _at_"http//
  • / An example type hierarchy /
  • subtypeOf( composer subtype , musician
    supertype )

/ Techquila hierarchy PSIs / hierarchical-relat
ion-type "Hierarchical relation type"
ical-relation-type" superordinate-role-type
"Superordinate role type" _at_"http//www.techquila
.com/psi/hierarchy/superordinate-role-type" sub
ordinate-role-type "Subordinate role type"
ate-role-type" / XTM superclass-subclass PSIs
/ subtypeOf hierarchical-relation-type
"Subtype of "Supertype of" / supertype
lass-subclass" subtype subordinate-role-type
"Subtype" _at_"http//
ore.xtmsubclass" supertype
superordinate-role-type "Supertype"
Dragon 2 Non-type hierarchies
  • Not all hierarchies are type hierarchies
  • For example
  • geographical containment
  • part of relationships
  • subject classifications
  • These relationships are not supertype- subtype
  • located in
  • part of
  • subtopic of
  • So again, be careful!

Norway is NOT a kind of Europe...
A piston is NOT a kind of submarine...
An opera is NOT a kind of music...
(No Transcript)
Topic Maps and Knowledge Organization
  • Keywords controlled vocabularies
  • Taxonomies, thesauri classifications
  • Indexes glossaries
  • Ontologies

Bibliographic languages
  • Work language
  • Author language
  • Title language
  • Edition language
  • Subject language
  • Classification language
  • Index language
  • Document language
  • Production language
  • Carrier language
  • Location language
  • Svenonius, Elaine (2000) The Intellectual
    Foundation of Information Organization. Cambridge,
    MA MIT Press (p.54)
  • Work languages
  • Work languages describe information entities,
    their intellectual (as opposed to physical)
    attributes, and relationships among them. (p.87)
  • Document languages
  • A document is a particular space-time embodiment
    of information a document language describes and
    provides access to this embodiment. (p.107)
  • Subject languages
  • A subject language is used to depict what a
    document is about. (p.127)

Two perspectives
  • Works have tended to be conflated with documents
  • So in practice there have been two kinds of
  • Document languages
  • describe the work and its manifestations
  • document-centric (or resource-centric), e.g.
  • document metadata (Dublin Core)
  • bibliographic records (MARC)
  • Subject languages
  • describe the subject space in which the work
  • subject-centric, e.g.
  • thesauri, taxonomies (ICD)
  • classification schemes (LCSH, DDC)
  • faceted classification (Colon)

  • Data about data
  • Information about documents
  • e.g. author, title, publisher, date, format,
  • Useful for managing the content
  • Especially suitable for librarians
  • Somewhat useful for searching
  • Especially for experts
  • Less useful for end-users
  • the user starts out wanting to know more about a
  • traditional metadata, however, focuses on the
  • if aboutness is provided at all, it gets squeezed
    into a single field

  • Primitive form of subject-based classification
  • The keywords are used to describe the subject
  • Cheap and simple… Folksonomies and tagging.
  • But also problematic because authors
  • misspell keywrods,
  • use different keywords/terms/tags for the same
    thing, and
  • use keywords that make no sense
  • Secondary problem
  • No way for the user to find out what keywords
    have been used
  • A keyword is a topic name

Controlled vocabularies
  • Solution create a list of legal keywords!
  • Requires somewhere to keep the list, and a
    process for new terms
  • Benefits
  • Solves problems of misspelling and duplicates
  • Disadvantages
  • Introduces some overhead (a flat list is
    difficult to manage)
  • Users can still search using the wrong terms
  • Users (and authors) still have difficulty finding
  • A controlled vocabulary is a well-defined set of
    topics with one name per topic

  • Organize the keywords into a tree
  • Most general at the top, more specific further
  • Common structure used by Yahoo!, etc.
  • The folder metaphor
  • file systems, email, favourites
  • Requires relationships between terms
  • Relationships state that one term is more
    specific than another
  • Advantage terms somewhat easier to find
  • Disadvantage real world does not fit neatly into
    a hierarchy
  • A taxonomy is a set of topics related through a
    specific type of hierarchical association

  • Like a taxonomy, but with some extensions
  • Also better defined there are ISO standards for
  • Relationship types
  • BT Broader term NT Narrower term
  • USE Preferred term UF Non-preferred terms
  • RT Related term
  • SN Scope note
  • A thesaurus is a set of topics related through
    particular, predefined association types
  • BT/NT (hierarchical) and RT (untyped,
  • (Scope notes are a kind of occurrence)
  • (USE and UF represent multiple names for the same

Faceted classification
  • Invented by S. R. Ranganathan in the 1930s
  • 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
  • A faceted classification is a collection of topic
  • Each hierarchy contains topics whose names are
    used as terms within a particular facet
  • XFML An XML interchange syntax for faceted
    classification inspired by Topic Maps

Expressivity progression
open model
  • Topic maps and RDF/OWL
  • use any types, properties, and relationships you
  • Faceted classification
  • multiple vocabularies, taxonomies or thesauri
    (one per facet)
  • Thesauri
  • more formal taxonomy still no topic types two
    association types
  • Taxonomy
  • terms arranged in a hierarchy no topic types
    single association type
  • Controlled vocabulary, folksonomies
  • just a list of terms no topic types no

fixed model
no model
Document-centric approaches
  • Traditional metadata is document-centric
  • Provides substantial descriptive power for
  • Allows connection into subject-based
  • Crucial for the management of content
  • However, users are most interested in the
  • Taxonomies, thesauri, and faceted classification
    are also document-centric
  • These are methods for subject-based
  • They provide hardly any descriptive power for

Subject-centric approaches
  • Topic maps are subject-centric
  • They provide great descriptive power for subjects
  • Good as finding aids, because subjects are what
    users care about
  • Documents can be treated as subjects
  • This enables topic maps to capture metadata as
  • It also enables topic maps to stitch metadata and
    subject-based classification together into one
    seamless whole
  • Topic Maps is the knowledge model par excellence
  • A subject-centric knowledge model that
    encompasses every other kind of knowledge
    organization model
  • Topic Maps can therefore be used to relate and
    combine taxonomies, indexes, thesauri,
    classifications, etc. etc.