Seamless Knowledge with Topic Maps - PowerPoint PPT Presentation

Loading...

PPT – Seamless Knowledge with Topic Maps PowerPoint presentation | free to download - id: ce1fc-ZDc1Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Seamless Knowledge with Topic Maps

Description:

Topic types: 'composer', 'city', 'opera' Association types: 'born in', 'composed by' ... Give me all composers that composed operas that were based on plays that were ... – PowerPoint PPT presentation

Number of Views:34
Avg rating:3.0/5.0
Slides: 54
Provided by: pamg150
Learn more at: http://heim.ifi.uio.no
Category:

less

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

Title: Seamless Knowledge with Topic Maps


1
Seamless Knowledge with Topic Maps
  • A Standard Model for Metadata, Taxonomies, Ontolog
    ies, and Knowledge Management

Steve Pepper Chief Strategy Officer,
Ontopia Convenor, SC34/WG3 Editor, XML Topic
Maps ltpepper_at_ontopia.netgt
2
Ontopia The Topic Maps Company
  • Our mission
  • To provide Topic Maps technology and services for
    information and knowledge management
  • Background
  • Established April 2000 out of STEP Infotek
  • Headquarters in Oslo, Norway
  • Partners in 13 countries around the world
  • Recognized leaders of the Topic Maps community
  • Products
  • Ontopia Knowledge Suite
  • Consultancy, training, and application
    development through partners

On'topia, 1999.f. Gr. onto- (being) Gr.
topos (place) see -IA. I. An imaginary world
in which knowledge is well organized. II. A
company that provides tools to help you realize
your own Ontopia…
Norway Partners Bouvet AS Lava Group
3
What is Topic Maps?
  • An International Standard for subject-based
    organization of information and knowledge
    management
  • More importantly…
  • What is Topic Maps used for?
  • (What are topic maps used for?)
  • Organizing large bodies of information
  • Capturing organizational memory
  • Representing complex rules and processes
  • Supporting concept-based eLearning
  • Managing distributed knowledge and information
  • Aggregating information and knowledge
  • etc…
  • Seamless Knowledge

4
Perspectives on Topic Maps
  • The information management perspective
  • A standard for subject-based organization of
    information in support of findability
  • The knowledge management perspective
  • A knowledge representation formalism optimized
    for use in information management the worlds
    first standard for KM
  • The library science perspective
  • A way to collocate all knowledge about a subject
    in particular its relationship to other
    subjects and to information resources

4
5
ISO 13250 Background and current status
  • Origins date back to early 1990s
  • Davenport Group ? GCARI / CAPH ? ISO
  • First Edition
  • International Standard (ISO/IEC 132502000)
  • Model and syntax based on SGML
  • Second Edition
  • XML Topic Maps (XML version for use on the Web)
  • ISO 13250 2003 (includes XTM)
  • Revised Edition
  • Multipart standard appearing in 2005
  • Includes data model, query language, constraint
    language

6
The TAO of Topic Maps
  • Topics Associations Occurrences
  • Taxonomies Metadata Ontologies

7
The core Topic Maps model
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 of Topic Maps are based on
    those of the back-of-book index
  • The same basic concepts have been extended and
    generalized for use with digital information
  • Envisage a 2-layer data model consisting of
  • a set of information resources (below), and
  • a knowledge map (above)
  • This is like the division of a book into content
    and index

8
(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, etc.
  • This is like the content of the book to which
    the back-of-book index belongs

9
(2) The knowledge layer
  • The upper layer consists of topics and
    associations
  • Topics represent the subjects that the
    information is about
  • Like the list of topics that forms a back-of-book
    index
  • Associations represent relationships between
    those subjects
  • Like see also relationships in a back-of-book
    index

composed by
composed by
Tosca
Puccini
Madame Butterfly
born in
Lucca
knowledge layer
10
Linking the layers through occurrences
  • The two layers are linked together
  • Occurrences are relationships with information
    resources that are pertinent to a given subject
  • The links (or locators) are like page numbers in
    a back-of-book index

composed by
composed by
Tosca
Puccini
Madame Butterfly
born in
Lucca
11
Summary of core concepts
Lets look at some TAOs in the Omnigator…
  • The TAO of Topic Maps

12
(No Transcript)
13
With this simple but flexible model you can
  • Represent subjects explicitly
  • Topics represent the things your users are
    interested in
  • Capture relationships between subjects
  • Associations provide user-friendly navigation
    paths to information
  • They also promote serendipitous knowledge
    discovery through browsing
  • Make information findable
  • Topics provide a one-stop-shop for everything
    that is known about a subject
  • Occurrences allow information about a common
    subject to be linked across multiple systems or
    databases
  • Represent taxonomies and thesauri
  • Associations may represent hierarchical
    relationships
  • 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
    memory

14
Topic Maps and taxonomies
  • Taxonomies are in at present
  • Reflects the need for subject-based
    classification to solve the problem of
    findability
  • No formal definition of the term taxonomy
    exists
  • Originally used to classify the world into
    hierarchically ordered taxons or classes
  • Used more broadly today for any hierarchically
    ordered classification
  • Most taxonomies consist of
  • A set of top-level terms, each of which defines a
    branch of the taxonomy
  • Topic/subtopic relationships between terms to
    form the tree
  • From the Topic Maps perspective
  • Taxonomies are simply sets of topics related
    together through hierarchical associations
  • Topic Maps can handle both simple and compound
    taxonomies
  • Compound taxonomies can be used for faceted
    classification
  • Using Topic Maps brings the benefits of
    standardization

15
Topic Maps and metadata
  • Document metadata is data about a document, e.g.
  • Properties like title, author, date,
    publisher, subject, rights, etc.
  • Dublin Core and PRISM cover most requirements for
    publishers
  • Such metadata can be expressed as a topic map
  • In this case, the document is the topic and its
    properties are names, occurrences, and
    associations with other topics
  • However, if this is all you need, Topic Maps
    could be over the top
  • (RDF offers a simpler, low-level model for this
    kind of metadata)
  • But document metadata is less useful for
    end-users
  • Most users want to locate information by subject
  • For them, the only really interesting metadata is
    the keywords
  • Topic Maps provides a way to turn lists of
    keywords into navigable knowledge structures
  • This is like metadata about the metadata
  • Topic Maps in combination with Dublin Core /
    PRISM metadata gives the publisher and the user
    the best of both worlds

16
Topic Maps and ontologies
  • The term ontology is used in many different
    ways
  • An ontology is the types and subtypes of
    concepts and relations that exist in some
    domain… John Sowa Knowledge Representation
    (Pacific Grove, 2000)
  • In Topic Maps the basic building blocks are
  • Topics e.g. Puccini, Lucca, Tosca
  • Associations e.g. Puccini was born in Lucca
  • Occurrences e.g. http//www.opera.net/puccini/bi
    o.html is a biography of Puccini
  • Each of these constructs can be typed
  • Topic types composer, city, opera
  • Association types born in, composed by
  • Occurrence types biography, street map,
    synopsis
  • All such types are also topics (within the same
    topic map)
  • Puccini is a topic of type composer … and
    composer is also a topic
  • A topic map thus contains its own ontology!

17
Five cool things to do with a topic map
  • Querying
  • Constraining
  • Filtering
  • Visualizing
  • Merging

18
Querying topic maps
  • Topic Maps is based on a formal data model
  • This means that topic maps can be queried, like
    databases
  • ISO 18048 Topic Maps Query Language (TMQL)
  • Companion to ISO 13250, currently being balloted
    in ISO
  • Allows more powerful use of taxonomies to
    retrieve information
  • Permits queries that would make Google boggle
    (see below)
  • TMQL is 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
    Shakespeare

19
Constraining topic maps
  • ISO 13250 itself provides no way to constrain
    topic maps
  • Examples of constraints
  • All persons must be born somewhere
  • A person may have died somewhere
  • Constraints are necessary in order to
  • Permit semantic validation of content
  • Ensure consistency
  • Enable more intuitive user interfaces
  • Simplify application development
  • ISO 19756 Topic Maps Constraint Language (TMCL)
  • Companion to ISO 13250, currently being balloted
    in ISO
  • Will interoperate with OWL (Web Ontology
    Language)
  • Ontopia has developed OSL for its customers
  • Demo of OSL in the Omnigator

20
Filtering, scoping and personalizing topic maps
  • Multiple world views
  • Reality is ambiguous and knowledge has a
    subjective dimension
  • Scope allows the expression of multiple
    perspectives in a single topic map
  • Typical application Combining related but
    divergent taxonomies
  • Contextual knowledge
  • Some knowledge is only valid in a certain
    context, and not valid otherwise
  • Scope enables the expression of contextual
    validity
  • Personalized knowledge
  • Different users have different knowledge
    requirements
  • Scope permits personalization based on personal
    references, skill levels, security clearance,
    etc.
  • Demo of scope-based filtering in the Omnigator

21
Visualizing topic maps
  • The network or graph structure of a topic map can
    be visualized for humans
  • This provides another view on information that
    can lead to new insights
  • Demo of visualization using Vizigator

22
Merging topic maps
  • Topic Maps can be merged automatically
  • You can always and in any situation take any two
    arbitrary topic maps and merge them to a single
    topic map
  • This cannot be done with databases or XML
    documents
  • The merge capability enables many advanced
    applications
  • Information integration across repositories
  • Sharing and reusing taxonomies
  • Automated content aggregation
  • Distributed knowledge management
  • The concept that makes merging possible is
    subject identity
  • Topic Maps has a robust mechanism for using URIs
    as identifiers

23
Principles of merging in Topic Maps
  • In Topic Maps, every topic represents some
    subject
  • The collocation objective requires exactly one
    topic per subject
  • 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…
24
How Topic Maps improves access to information
  • Intuitive navigational interfaces for humans
  • The topic/association layer mirrors the way
    people think
  • Powerful semantic queries for applications
  • A formal underlying data structure
  • Customized views based on individual requirements
  • Personalization based on scope
  • Information aggregation across systems and
    organizations
  • Topic Maps can be merged automatically…

25
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)

26
Taxonomy management
  • Addresses the problem of managing unstructured
    content
  • Organization by subject is seen as the solution
    because thats how users search
  • More and more companies are looking into and
    developing taxonomies
  • A taxonomy is a simple form of topic map
  • Topic Maps provides subject-based organization
    de-luxe
  • 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
  • The Dutch Tax and Customs Administration
    (Belastingdienst) uses the OKS as the basis of a
    taxonomy management system
  • http//www.idealliance.org/papers/dx_xmle04/papers
    /04-01-03/04-01-03.html
  • This capability can also be added to
    Content Management Systems

27
Metadata management
  • On behalf of the Norwegian Government
    Administration Services Lava Group is building a
    metadata server
  • Metadata for government publications will be
    managed using the OKS
  • Will be used in the central public information
    portal (ODIN)
  • (System currently under development)
  • The system provides
  • Authoring system used by the editors
  • Vocabulary Editor for adjusting the metadata
    vocabulary used
  • Metadata Export to various systems
  • Web services based on the metadata
  • Unique identifiers for documents
  • Unparallelled future flexibility

ODIN
Metadata server (OKS)
Exported subjects
ASCII-export
Lovdata
MUP
28
Semantic portals
  • Topic Maps as Information Architecture for web
    delivery applications
  • Web sites, portals, corporate intranets, etc.
  • Site structure is defined as a topic map
  • Each page represents a topic (subject-centric)
  • User-friendly navigation paths defined by
    associations
  • Topics used to classify content
  • High potential for portal connectivity using
    TMRAP
  • Permits evolution towards Knowledge Management
    solutions
  • The OKS has been used to create portals, e.g.
  • Kulturnett.no (Norwegian public sector portal to
    cultural information) www.kulturnett.no
  • Apollon (University of Oslo research magazine)
    www.apollon.uio.no

29
Enterprise information integration Theory
  • Topic Maps are designed for ease of merging
  • Generate topic maps from structured data
  • (Or create topic map views of that data)
  • Classify content according to a taxonomy topic
    map
  • Merge the topic maps to provide a unified view of
    the whole
  • Topic maps are easy to filter
  • Create personalized views of the unified
    information model
  • Typing topics and scope provide built-in criteria
    for filtering
  • Advantages
  • Consolidated access to all related information
  • Does not require migration of existing content
  • Standards-based

30
Enterprise information integration Practice
  • Starbase is using the OKS in an internal project
    called Elmer
  • This project is building an integration server
    for software information
  • Multiple disparate applications hold related data
  • Building a unified topic map layer on top makes
    it possible to search across repositories
  • Provides data integration without changing the
    underlying applications
  • Access to information provided through a portal
  • Straightforward navigation interface
  • Querying, both full-text and structured
  • Topic maps drives integration with MS Office
    Smarttags
  • Terms known from Elmer are highlighted
  • (Names of topics used as a vocabulary)
  • Appear as links back into the portal

31
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
  • 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 the OKS
  • An application of the Web Editor Framework
  • Demonstrates user-friendliness of TM editing

32
Business process modelling
  • A multinational petrochemical company uses the
    OKS 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 case of
    a more general process)

33
Product configuration
  • A Scandinavian telecom company uses the OKS 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
  • Different features apply in different regions
  • etc.
  • The network of dependencies is already quite
    complex
  • Now throw versioning into the mix!
  • Managing all this data is not easy
  • The system models dependencies in a topic map
  • Product configuration engineers use this to
    configure products using a very user-friendly
    interface
  • The system is driven by inference rules
  • These work on the topic map
  • Easily capture complex logic
  • Also integrates with product documentation

Features
Product families
Versioning
System data
Products
34
Business rules management
  • The US Department of Energy has used the OKS 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
  • 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 the complexity
    of the rules
  • Status as ISO standard ensures stability and
    longevity

Master topic
Guidance topic
Parent topic
Child topic
Derived topic
Responsible person
Concept
Workflow state
35
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 access to the knowledge
    base
  • Whitney online
  • Houson offline (for use in emergencies)

36
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.

37
Dynamic Content Aggregation
  • An Application of Seamless Knowledge
  • Automatic Portal Integration
  • Topic Maps Remote Access Protocol

38
Semantic portals
  • Think of Topic Maps as an Information
    Architecture
  • Topic Maps is an ideal model for portals and
    other forms of web-based information delivery
  • The basic concept is to have the topic map drive
    the portal
  • Not just a navigational layer on top of something
    else
  • The very structure of the portal is a topic map
  • All content is organized around topics
    (subject-centric organization)
  • Each page represents a topic (we call this a
    Topic Page)
  • Topics act as points of collocation
  • They provide a one-stop shop for everything
    that is known about a particular subject
  • Navigating the portal Navigating the topic map
  • Associations provide very intuitive navigation
    (As we may think)

39
A topic page
40
(No Transcript)
41
(No Transcript)
42
The rise and rise of semantic portals
  • In Norway, this concept has been put into
    practice on a scale that is verging on the
    industrial, especially among government agencies
  • At present there are over a dozen, with more on
    the way
  • Some semantic portals in Norway
  • In production
  • http//www.itu.no http//www.luna.itu.no (Ministry
    of Education)
  • http//www.forskning.no http//www.nysgjerrigper.n
    o (Research Council of Norway)
  • http//forbrukerportalen.no (Consumers
    Association)
  • http//www.skifte.no (Norwegian Defence)
  • http//matportalen.no (Ministry of Agriculture)
  • http//www.udi.no (Ministry of Justice)
  • http//www.kulturnett.no (Ministry of Culture)
  • Under development
  • http//www.hoyre.no (Norwegian Conservative
    Party)
  • Skatteetaten (Tax Office)
  • Statsministerens kontor (Office of the Prime
    Minister)
  • Statistisk Sentralbyrå (Central Bureau of
    Statistics)
  • IFE/Halden (Nuclear Reactor Project)
  • etc.
  • etc.

43
Towards seamless knowledge
  • As the number of portals multiplies, the amount
    of overlap increases…
  • Take these three portals as an example
  • forskning.no (Research Council web site aimed at
    young adults)
  • forbrukerportalen.no (Public site of the
    Norwegian Consumer Association)
  • matportalen.no (Biosecurity portal of the
    Department of Agriculture)

44
Genetically modified food at forskning.no
45
Genetically modified food at Forbukerrådet
46
Genetically modified foodstuffs at Matportalen
47
Three semantic portals One common subject
? one virtual portal
with seamless navigation in all directions
48
Achieving seamless knowledge
  • Very little is required for these portals to
    achieve a simple but effective form of Seamless
    Knowledge
  • They have already achieved subject-centric
    organization of their content
  • Without this, Seamless Knowledge is beyond reach
  • Without this, Seamless Knowledge is beyond reach
  • From a technical perspective, only two additional
    pieces are required to complete the puzzle
  • 1 An identity mechanism
  • To make it possible to know when their subjects
    are the same
  • Published subjects solve this problem
  • A flexible and robust mechanism for using URIs as
    global identifiers
  • See www.oasis-open.org.
  • 2 An exchange protocol
  • To enable information to be requested and
    exchanged automatically
  • Ontopia has developed Topic Maps Remote Access
    Protocol

49
Topic Maps Remote Access Protocol (TMRAP)
Hi! Do you know the subject genetically modified
food?
The actual question was Is the
subject http//psi.forskning.no/food/gm-food known
in your system?
? http//matportalen.no/Matportalen/Emner/gmo
This scenario (called VISIT) is supported by TMRAP
50
The Omnigator Rap demo (Part 1 VISIT)
  • Two Omnigators are running on this machine
  • Different browsers (Opera and Internet Explorer)
  • Different skins (Ontopia National Colours and
    Vive Québec)
  • Different names pepper poivre
  • Different TMs (Italian Opera and Various
    Geographical TMs)

51
Simulation of VISIT demo
  • View Topic Page for Japan in _at_pepper
  • Go to Manage page in _at_poivre and load Scripts and
    Languages
  • Reload Topic Page in _at_pepper
  • Links to Remote Topic Page automatically inserted
  • Click on VISIT and navigate to the Topic Page in
    _at_poivre
  • Go to Manage Page, load CIA World Factbook, go
    back to Japan Topic Page in _at_poivre, VISIT
    _at_pepper, note new Remote Topic Link…
  • etc.

etc.
52
VISIT Some considerations
  • The functionality is deceptively simple, yet
    potential very powerful
  • From the users point of view the VISIT links
    might have been hand-coded (there is no visible
    difference)
  • The cool thing is that they are generated
    entirely automatically
  • This is dynamic content aggregation in practice!!
  • NO MAINTENANCE of cross-site links is required
  • Solves the publishers cross-site link management
    problem with technology
  • And we can go a step further with relatively
    little effort
  • Rich data based on Topic Maps can be merged …
  • … so we can exchange not only links,
  • but also whole chunks of rich data
  • We call those chunks topic maplets
  • This is how it works…

53
TMRAP GET scenario using topic maplets
Hi! What do you know about genetically modified
food?
The actual question was What information do
have about http//psi.forskning.no/food/gm-food i
n your system?
This scenario (called GET) provides another level
of content aggregation
54
Simulation of GET demo
  • View Topic Page for Japan in _at_pepper
  • Go to Manage page in _at_poivre and load both
    Scripts and Languages and CIA World Factbook
  • Reload Topic Page in _at_pepper
  • Links to Remote Topic Pages automatically
    inserted
  • Click on GET for each one and see the set of
    information be augmented by the addition of
    names, associations and occurrences from the
    remote topic maps.

55
GET Some considerations
  • The functionality is even more powerful…
  • The seamlessness factor is much greater
  • (In fact we have dumbed it down in this demo
    in order to show what is actually going on The
    GET functionality could be activated
    automatically)
  • Application areas are slightly different
  • Useful when seamlessness is more important and
    branding issues less important
  • E.g., within a corporate or government environment

56
Possible use cases
  • Legal publisher can offer SLA-tiered licensing
  • Short-term VISIT permissions highlight what the
    subscriber would get in addition to current level
    of licensing
  • Turned into a GET if the subscriber buys the
    additional content
  • Government agencies can make all of their portals
    interconnected
  • Without ANY need to maintain those connections
  • Simply apply use of IDs rigorously and maintain
    registry of available websites
  • Publishers in general can license their
    ontologies to customers
  • Customers use ontology to organize their own
    content and business processes, and integrate
    seamlessly with publishers content
  • Important tool for customer retention

57
Topic Maps and the Semantic Web
  • Seamless Knowledge is not the same as the
    Semantic Web
  • But there is a lot of overlap and potential
    synergy
  • Topic Maps and RDF/OWL are optimized for
    different purposes
  • RDF/OWL is about creating an artificially
    intelligent Web
  • Optimized for software agents and reasoning
  • Topic Maps is about aggregating information and
    knowledge
  • Optimized for humans and findability
  • However, interoperability at the data level is
    certainly possible
  • RDF data can be viewed as Topic Maps (and vice
    versa)
  • A W3C working group is developing guidelines
  • RDF/Topic Maps Interoperability Task Force
  • http//www.w3.org/2001/sw/BestPractices/RDFTM/
  • One very plausible strategy for publishers is
  • RDF for metadata Topic Maps for subject
    classification and user interface
  • Demo of RDFTM interoperability in the Omnigator

58
Conclusion
  • Subject-based classification provides a solution
    to the findability problem
  • Topic Maps are the international standard of
    choice for doing this
  • Topic Maps can represent
  • taxonomies
  • thesauri
  • indexes
  • metadata
  • ontologies
  • …all in a single, intuitive model
  • Any questions?

59
What now?
  • Read The TAO of Topic Maps
  • Download the Omnigator
  • Learn LTM and create your own first topic map
  • Consider doing a thesis on a Topic Maps-related
    subject
  • Attend Ontopias training class
  • June 6th Full day introduction to Topic Maps
  • June 7th Ontology design for Topic Mappers
  • (other days cost money)
  • Make sure you register!
  • All details at www.ontopia.net

60
RDF and Topic Maps
  • Similarities
  • Differences
  • Interoperability

61
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

62
How the two families stack up
OWL
TMCL
RDF Schema
SPRQL
TMQL
QUERY
Topic Maps
ORG SYNTAX MODEL REASONING
ORG SYNTAX MODEL CONSTRAINTS
RDF
XML
HyTM
XTM
LTM
RDF/XML
N3
RDF/A
ISO Seamless Knowledge
W3C Semantic Web
63
Similarities that cry out for unification
  • Striking similarities
  • 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
  • This lead to calls for unification
  • Free-for-all between the two Erics at Extreme
    Markup 2000
  • Michael Sperberg-McQueen suggested locking the
    RDF people and the Topic Maps people in a room
    together until they had harmonized the two…

64
But there are important differences too…
  • Different roots
  • Topic Maps has its roots in traditional finding
    aids (indexes, thesauri, etc.)
  • RDF has its roots in document metadata and formal
    logic
  • 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
    agents
  • Topic Maps Findability and knowledge integration
    for humans
  • So unification never happened
  • But the perception of rivalry is a cause for
    confusion

65
Its time to move beyond bigotry
  • Lets look for the synergies instead!
  • Both families have user communities
  • Neither standard will go away anytime soon
  • Common interest in the success of semantic
    technologies
  • Semantics are hard enough to explain to the
    market as it is
  • A standards war will indeed lead to a Plague o
    Both Our Houses…
  • RDF and Topic Maps are different
  • Different strengths, different weaknesses
  • Lets recognize this
  • And lets go for interoperability
  • Thats the goal of RDFTM…

66
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 (WG
    Note)
  • Guidlines for RDF/Topic Maps Interoperability (WG
    Note or Recommendation)
  • First draft of Survey recently delivered to WG
  • http//www.w3.org/2001/sw/BestPractices/RDFTM/surv
    ey
  • First draft of Guidelines for Extreme Markup 2005
  • http//www.extrememarkup.org/

67
RDF or Topic Maps? Some rules of thumb
  • The basic premise
  • RDF is more low-level oriented towards machines
  • Topic Maps is more high-level oriented towards
    humans
  • OWL is a step beyond oriented towards artificial
    intelligence
  • Do you simply want to encode document metadata?
  • RDF is an ideal model for assigning properties to
    documents (e.g. Dublin Core, PRISM) you
    probably 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
    classification?
  • Go straight for Topic Maps or, if you already
    use RDF for metadata, view it as a topic map in
    conjunction with a TM-based taxonomy or subject
    classification
  • Do you want to enable applications based on
    software agents?
  • Use RDF/OWL on a foundation of Topic Maps-based
    knowledge organization
About PowerShow.com