?? INF3600 Topic Maps ?? - IT-SLP

ONTOPEDIA The Identity of Everything

?? INF3600 Topic Maps ?? Subject-Centric Computing. A New Paradigm.

Steve Pepper [email protected] IFI, 2008-05-06

http://www.ontopedia.net/pepper/slides/IFI.ppt

www.ontopedia.net


Roadmap z z

What Topic Maps is not What Topic Maps is –

The standard

–

The model

–

The demo

–

The applications

–

The future


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What Topic Maps is NOT z

Nothing to do with traditional cartography –

The name is a metaphor: z z

z

Not a technology for visualizing knowledge –

z

Although it is possible to visualize a topic map

Not a technology for building portals –

z

A (geographical) map is a (selective) representation of a terrain A topic map is a (selective) representation of human knowledge

Although that’s the most visible kind of application today

Not an alternative to the Web –

Topic Maps adds value to the Web


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So what is Topic Maps? z

A family of ISO standards –

z

A semantic technology –

z

Annual conferences, academic journal, etc.

A growth technology in Norway –

z

The harbinger of a paradigm shift in information management

A field of scientific research –

z

Used to represent and interchange a certain kind of knowledge

An enabler for subject-centric computing –

z

ISO/IEC 13250, DIS 18048, DIS 19756

Increasingly used in public and private sector

A good candidate for being taught at UiO...


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Brief History of Topic Maps 1992: Original insight leads to development of Topic Navigation Maps. 1995: Topic Maps accepted as a work item in ISO. 2000: Publication of ISO/IEC 13250 (SGML-based syntax). 2001: Publication of 2nd Edition (XML-based syntax). First TM-driven web site launched in Norway.

2002: First Norwegian conference. More tools and applications. 2005: First academic conference. 2007: First international users conference. Publication of data model, updated syntax, etc.

2008: Standardization, research, outreach and adoption continues. http://www.ontopedia.net/pepper/slides/IFI.ppt

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The ISO specifications z

ISO/IEC 13250: Topic Maps – – – – – – –

z

z

z

13250 Part 1: Overview and Basic Concepts Part 2: Data Model CTM GTM Part 3: XML Syntax 13250-6 13250-7 Part 4: Canonicalization Part 5: Reference Model Part 6: Compact Syntax XTM CXTM Part 7: Graphical Notation 13250-3 13250-4

ISO/IEC DIS 18048: Topic Maps Query Language ISO/IEC DIS 19756: Topic Maps Constraint Language ISO/IEC DTR 29111: –

Expressing Dublin Core Metadata Using Topic Maps


TMCL 19756 TMDM 13250-2 TMQL 18048 TMRM 13250-5 www.ontopedia.net


Topic Maps and Hypertext


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Vannevar Bush and Hypertext

Doug Engelbart 1962 Augmenting Human Intellect NLS / AUGMENT

Vannevar Bush 1945 As We May Think Memex Ted Nelson 1965 “Hypertext” Xanadu http://www.ontopedia.net/pepper/slides/IFI.ppt

Bill Atkinson 1987 HyperCard

Tim Berners-Lee 1989 Information Management: A Proposal World Wide Web

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“As We May Think” z

Concerned with the problem of finding information –

Existing technology hopelessly out of date:

–

The amount of information is being “expanded at a prodigious rate”, but the means we use to find it is “the same as was used in the days of square-rigged ships”

–

The solution is to get away from hierarchical systems of organization and adopt new techniques that reflect how the brain works


Vannevar Bush 1945 As We May Think MEMEX

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Associative thinking “The human mind … operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain… The speed of action, the intricacy of trails, the detail of mental pictures, is awe-inspiring beyond all else in nature.” Vannevar Bush: As We May Think (1945) http://www.ontopedia.net/pepper/slides/IFI.ppt

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Memex (memory extender)

A “sort of mechanized private file and library” http://www.ontopedia.net/pepper/slides/IFI.ppt

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Memex (memory extender) z

Consists of a desk containing – – – – – –

z

a very large set of documents stored on microfilm screens on which those documents are projected a device for photographing new documents a mechanism for retrieving documents at the push of a button the ability to create links between documents the ability to build trails through documents, add comments to documents, insert new documents, etc.

Note how everything revolves around documents


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Is this how you think?


z

Is your head full of little documents all hyperlinked together?

z

I doubt it !

z

Mine certainly isn’t !

z

We don’t think in terms of hyperlinked documents; we think in terms of concepts, and associations between concepts

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How we really think WWW

Berners-Lee

Engelbart

Bush As We May Think

AUGMENT

Hypertext

MEMEX Nelson

Xanadu

NLS

z z z z z


Documents are about subjects Those subjects exist as concepts in our brains They are connected by a network of associations This is how we store knowledge Documents are just a representation of some part of that knowledge www.ontopedia.net


Bush – right and wrong z

Vannevar Bush was right that people think associatively

z

He was right that organizing information in this way would make it easier to find

z

But he was wrong in adopting a document-centric approach to the problem

z

His basic idea – organize information “as we may way think” – was a great inspiration to Engelbart, Nelson, Atkinson, and Berners-Lee


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Barking up the wrong tree z

But the Memex sent them all off in the wrong direction Hypertext has been barking up the wrong tree ever since

z

And the Web, magnificent as it is, has made things “worse”

z


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“As We May Think” (63 years on) z

Concerned with the problem of finding information –

Existing technology hopelessly out of date:

–

The amount of information is being “expanded at a prodigious rate”, but the means we use to find it is “the same as was used in the days of square-rigged ships”

card catalogs –

The solution is still to get away from hierarchical systems of organization and adopt new techniques that reflect how the brain works

–

That solution has to be subject-centric, not document-centric like the Web


Vannevar Bush 1945 As We May Think MEMEX

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Which brings us to Topic Maps composed by composed by

WWW

Berners-LeeEngelbart

Tosca

Bush As We May Think

AUGMENT

Hypertext

MEMEX Nelson

Puccini

born in

Xanadu

Madame Butterfly

NLS

Lucca

z

knowledge layer information layer

What’s special about it? –

#1 The TAO* model corresponds to how people think * Topics + Associations + Occurrences


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The TAO of Topic Maps

Topics Associations Occurrences


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The basic model z z z

z

Core concepts based on the back-of-book index Extended and generalized for use with digital information 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

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

(index) knowledge layer information layer (content)


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(1) The information layer z

z

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 information layer


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(2) The knowledge layer z

The upper layer consists of topics and associations –

Topics represent the subjects that the information is about z

–

Like the list of topics that forms a back-of-book index

Associations represent relationships between those subjects z

Like “see also” relationships in a back-of-book index composed by

composed by

Tosca Puccini

born in

Lucca http://www.ontopedia.net/pepper/slides/IFI.ppt

Madame Butterfly knowledge layer www.ontopedia.net


Occurrences link the layers z

The two layers are linked together –

–

Occurrences are relationships with information resources that are pertinent to a given subject

composed by composed by

Tosca Puccini

The links (or locators) are like page numbers in a back-of-book index


born in

Lucca

Madame Butterfly knowledge layer information layer

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Summary of core concepts Let’s look at some TAOs in the Omnigator…

A pool of information or data, and a knowledge layer consisting of •

Topics –

•

composed by

Associations –

•

composed by

a set of topics representing the key subjects of the domain in question representing relationships between subjects

Tosca Puccini

born in

Occurrences –

links to information that is somehow relevant to a given subject

Madame Butterfly

Lucca knowledge information

= The TAO of Topic Maps


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How the Omnigator works

Omnigator topic map

http

Ontopia Topic Map Engine

J2EE Web Server e.g. Tomcat

pages

Web Server Java Runtime Environment http://www.ontopedia.net/pepper/slides/IFI.ppt

Browser www.ontopedia.net


Omnigator interface (multiple) types

current topic

multiple names multiple typed occurrences

multiple typed associations

Demo


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Typing topics z

z

z

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/bio.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 –

These are what constitute the ontology of the topic map


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What Topic Maps can do z

Represent subjects explicitly –

z

z

Topics represent the “things” your users are interested in – or know about

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


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What Topic Maps can do (cont.) z

z

Represent taxonomies and thesauri –

Associations may represent hierarchical relationships

–

Topic Maps permits multiple, interlinked hierarchies and faceted classification

Transcend simple hierarchies –

z

Rich associative structures capture the complexity of knowledge and reflect the way people think

Represent human knowledge –

A topic map can embody organizational (or “corporate”) memory

–

Topic Maps is a knowledge management technology as well as an information management technology


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Four cool things to do with a topic map Querying Filtering Visualizing Merging


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Querying topic maps z

Topic Maps is based on a formal data model –

z

z

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 Ontopia’s query language tolog –

z

This means that topic maps can be queried, like databases

(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”


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Semantic full-text search z

Traditional full-text indexing has its limitations –

Google is great, but z z

z

The problem is one of precision vs. recall –

z

Full-text indexes are based only on names

Homonyms og polysemes (lead to low “precision”) –

The same name can mean many things z

z

Paris (France, Texas, Trojan hero, botany, Reality TV, …)

Synonyms (lead to low “recall”) –

One subject can have many names – even in the same language z

z

it doesn’t always give you what you want it always gives you more than you want

genetically modified food, GM food, genetically modified foodstuffs

Topic Maps can add semantic precision


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Capturing context z

A topic map is a knowledge base consisting of a set of assertions about the world – –

z

Contextual knowledge – –

z

Names, occurrences, associations are collectively known as statements Each statement can be “scoped” Some knowledge is only valid in a certain context, and not valid otherwise Scope enables the expression of contextual validity

Multiple world views – –

Reality is ambiguous and knowledge has a subjective dimension Scope allows the expression of multiple perspectives in a single Topic Map


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How scope works z

We make “statements” about topics –

Names, occurrences, associations

z

Every statement is valid within some context

z

This can be captured using scope –

–

–

the name “Allemagne” for the topic Germany in the scope “French” a certain information occurrence in the scope “technician” a given association is true in the scope (according to) “Authority X”

occurrence occurrence name name name

T

association role

Filtering by scope association role association role


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Applications of scope z

Multiple perspectives in a single topic map –

z

Representing contextual validity –

z

Ditto

Traceable knowledge aggregation –

z

Capture the complexity of the real world

Merge topic maps and retain information about provenance

Personalized knowledge –

Deliver filtered subsets of the topic map based on user needs


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Visualizing topic maps z

The network or graph structure of a topic map can be visualized for humans

z

This provides another “view” on information that can lead to new insights


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Merging topic maps z

z

z

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 documents

Merging enables many advanced applications –

Information integration across repositories

–

Sharing and reusing taxonomies

–

Automated content aggregation

–

Distributed knowledge management

Merging possible due to subject identity –

Robust mechanism for using URIs as identifiers...


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Principles of merging z z

By definition: Every topic represents exactly one subject Our goal: Every subject represented by just one topic 1.

When two topic maps are merged, topics that represent the same subject should be merged to a single topic

2.

When two topics are merged, the resulting topic has the union of the characteristics of the two original topics occurrence occurrence

name name name

occurrence name

T

association role

association role association role


T

name association Merge the two topics together... ...and the resulting topic has the union role of the original characteristics second topic (inin another topic (DemoAof merging the Omnigator…) map) “about” the same subject www.ontopedia.net


A vision: seamless knowledge z

Starting with ITU in 2001, Norway has seen an explosion in the number of portals that are based on Topic Maps –

z

As the number of portals multiplies, the amount of overlap increases… –

z z z z

Today there are dozens, especially in the public section

The potential for integration is … mind-blowing

Take these three portals as an example: forskning.no (Research Council web site aimed at young adults) forbrukerportalen.no (Norwegian Consumer Association) matportalen.no (Biosecurity portal of the Department of Agriculture)


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Genetically modified food at forskning.no


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Genetically modified food at Forbukerrådet

•Terefe Badenod


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Genetically modified foodstuffs at Matportalen


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Three portals – one subject

Î one “virtual portal” with seamless navigation in all directions


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Making information findable z

Intuitive navigational interfaces for humans –

z

Powerful semantic queries for applications –

z

A formal underlying data structure

Customized views based on individual requirements –

z

The topic/association layer mirrors the way people think, learn and remember

Personalized information delivery using scope

Information aggregation across systems and organizations –

Topic Maps can be merged automatically


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Semantic Portals Taxonomy Management Metadata Management

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


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Semantic portals z

Topic Maps as the Information Architecture –

z

z z

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 associations

–

Topics used to classify content

Potential for subject-based portal connectivity Smooth evolution into Knowledge Management solutions


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topicmaps.com

100% topic map-driven


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Highly “intertwingeled” – yet still easy to navigate


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So is TM a portal technology? z z

z

No, it’s not Many people think so –

But it wasn’t invented as such

–

It just turned out to be ideal for the purpose, because...

The underlying model is “as we may think” –

z z

That model is subject-centric, not document-centric

Until recently most applications of Topic Maps were portals Now they are not – as the recent conference showed –

Check out the program and slides at http://www.topicmaps.com/tmc/program.jsp?conf=TM2008


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Taxonomy management z

For managing unstructured content –

z

A taxonomy is a simple form of topic map –

z

z

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

Dutch Tax and Customs Administration (Belastingdienst) uses Topic Maps as the basis of a taxonomy management system –

z

Organization by subject – because that’s how users search

http://www.idealliance.org/papers/dx_xmle04/papers/04-01-03/04-01-03.html

Capability can be added to any Content Management System


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Metadata management

z

–

Management of metadata for government publications

–

Used in the central public information portal (ODIN)

Indexes ODIN Metadata …

ODIN

Primary goal –

z

FAST Search engine

A Metadata Server based on Topic Maps

Engine

z

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 regjeringen.no –

Metadata server (TM)

Logistics

Exported subjects

MUP

ASCII-export

Lovdata

Solution based on Topic Maps


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Enterprise information integration z

Topic Maps are designed for ease of merging –

–

z

Easy to filter –

z

Generate topic maps from structured data (or create topic map views of that data) Merge topic maps to provide a unified view of the whole Create personalized views of this unified model

Advantages: – – –

Consolidated access to all related information No need to migrate existing content Standards-based


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Enterprise information integration z

Example: Elmer project at Starbase (Borland) –

z

Multiple disparate applications hold related data – –

z

Unified topic map layer enables search across repositories Data integration without changing the underlying applications

Portal interface – –

z

Integration server for software information

Intuitive navigation Full-text and structured queries

Elmer

Bug

C++ class

T

caused by

T

breaks

Requirement

T

Smarttags integration – –

Elmer terms (topic names) highlighted Provide links into the portal Source repository


Bug database

Requirements DB www.ontopedia.net


E-learning: BrainBank z

Topic maps are associative knowledge structures –

z

Students describe what they have learned –

z

–

captured, named, described associated with other concepts

Students are able to – – –

z

Pilot users: 11-13 year olds

Key learning concepts are –

z

They reflect how people acquire and retain knowledge

capture the essence of a subject describe what they have learned keep track of their knowledge

Teachers are able to –

monitor students’ understanding


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Business processes z

Multinational petrochemical company – –

z

Processes are modelled in terms of –

z

Uses TMs to manage business process models Flexible model allows arbitrary relationships to be captured easily 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)


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Product configuration z

z

z

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 –

z

Product configuration engineers use this to configure products using a very user-friendly interface

Product families

Features

Versioning

System is driven by inference rules –

These work on the topic map

–

Easily capture complex logic

–

Also integrates with product documentation


Products

System data

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Business rules z

US Department of Energy: Rules for security classification – – –

z

Guidance topics form a complex web of relationships – –

z

Captured in a topic map (KB) Concepts connected to if-then-else rules

Master topic

KB used with inference engine –

–

z

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 documents

automatically classifies information (documents, emails, ...), and "redacts" information (PDF, email, ...)

Parent topic

Guidance topic

Child topic

Benefits: –

–

Model expressive enough to capture complexity of the rules ISO standard = stability & longevity


Derived topic

Responsible person

Concept Workflow state

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IT assets z

University of Oslo: Management of IT assets –

z

Used to answer questions like – – –

z

If operating system Z is upgraded, what apps are affected? Service X is down, who do I call? Houdini If I take Y down, what else goes?

Uses composite topic map – –

z

Servers, clusters, databases, etc. described in a TM (KB)

Partly autogenerated Partly handcoded

• Syntax control • OKS schema validation • Versioning with CVS

Two applications – –

Whitney: online Houston: offline (for use in emergencies)

Whitney

UIOTM FW OKS API

Navigator framework

OKS Engine

XTM

RDBMS backend

usit.ltm (handcoded)

oracle.ltm (generated)


CVS

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Manufacturing assets z z z

US Department of Energy Topic map describes Y-12 manufacturing facility Provides overview of –

equipment,

–

processes,

–

materials required,

–

parts already built,

–

etc.


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Topic Maps as a paradigm shift z z z

z z

z z

Topic Maps started out as a way to merge indexes It turned into a knowledge representation formalism But its significance is far greater Now the flag-bearer for subject-centric computing A paradigm shift in how we use computers Cf. object-oriented programming... ...and Copernicus


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Object-oriented programming z

z

z

Response to 1960’s software crisis –

Computer programs more and more complex

–

Difficult to maintain software quality

Code simulates the world (as perceived by a human) –

Objects represent real-world concepts (cf. topics)

–

They are grouped into classes (cf. topic types)

–

Data structures capture relationships between objects (cf. associations)

Represented a paradigm shift in programming –

OO languages now near universal (Java, C#, Ruby, Python, ...)


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The heliocentric revolution z

For 1,000s of years people thought that the sun revolved around the earth

z

In 1543 Copernicus changed all that

z

z

His heliocentric theory turned our understanding of the universe inside out. This was another paradigm shift


Sun

Earth

Earth

Sun

(Actually some Greek, Indian and Muslim scholars knew better, but the view of Aristotle, Ptolemy and the Christian Church was dominant) www.ontopedia.net


Subject-centric computing z

Today we face a similar situation in computing and information management

z

Computers are at the centre of our information universe

z

Applications and documents revolve around them

z

The subjects we’re really interested in are nowhere to be seen

z

Or at least, nowhere to be found


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Computing “as we may think” z

This is wrong, because it does not reflect how humans think

z

Humans think in terms of subjects, concepts, ideas

z

We must put subjects at the centre, because that’s what we’re really interested in

z

This is the essence of subject-centric computing

z

It really is a paradigm shift –

z

Topic Maps is showing the way


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INF3600 Topic Maps – why? z

Because it is an important information technology –

z

Because it is largely a Norwegian innovation –

z

No shortage of jobs for people with experience of Topic Maps

Because it is a field that needs a lot of research –

z

Norwegian academia has an opportunity to lead the world

Because Norwegian industry needs expertise –

z

Only time will tell how important it is

The foundation is in place, but much remains to be done

Ideal for IT-SLP (“applied cognitive science”) –

Also relevant for other disciplines (media science, linguistics, psychology, law, etc.)


www.ontopedia.net


INF3600 Topic Maps – what? 1. Basic concepts –

Topics, associations, occurrences

2. Creating a topic map –

Linear Topic Maps notation (*)

3. Areas of application –

EII, portals, KM, E-learning, etc.

4. Advanced concepts –

Scope, reification, merging

5. Subject identity –

Published subjects

6. The Topic Maps Data Model –

ISO/IEC 13250 Part 2

7. Designing an ontology –

8.

Interactive session


Ontology-driven editing –

9.

Configuring and using Ontopoly (*)

The XTM Syntax –

ISO/IEC 13250 Part 3

10. Topic Maps and RDF/OWL –

Relationship to the Semantic Web

11. Knowledge organization –

Taxonomies, thesauri, metadata

12. Query languages –

tolog, TMQL

13. Automatic topic map creation –

Generating TMs from other data

14. Constraint languages –

Ontopia Schema Language, TMCL

www.ontopedia.net


Topic Maps and the Semantic Web


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Topic Maps and the Semantic Web z

Some people think RDF/OWL and Topic Maps are competitors – –

z

I do not think this is not true I think they complement each other

The Semantic Web gets much more publicity 1. W3C can bask in the glamour of the Web 2. RDF and OWL appealed immediately to academics

z

RDF/OWL Topic Maps

Romeo and Juliet

But why do people think they compete? 1. 2. 3. 4.

RDF/OWL and Topic Maps have a number of similarities They stem from rival organizations (W3C and ISO) There are a few bigots Most people do not understand the difference...


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The are superficial similarities z

Both “extend” XML into the realm of semantics

z

Both allow assertions to be made about things in the real world

z

Both define abstract, associative (graph-based) models

z

Both have URI-based models of identity

z

Both allow forms of inferencing or reasoning

z

Both have XML-based interchange syntaxes

z

Both have constraint languages and query languages But they are also different in some crucial respects...


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But the differences are significant z

z

z

Different roots –

Topic Maps has its roots in traditional finding aids (indexes, thesauri, etc.)

–

RDF/OWL 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 –

z

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


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The Most Crucial Differences ¾ RDF/OWL is for machines; ¾ Topic Maps is for humans. ¾ RDF/OWL is optimized for inferencing; ¾ Topic Maps is optimized for findability. ¾ RDF/OWL is based on formal logic; ¾ Topic Maps is not based on formal logic. ¾ RDF/OWL is to mathematics as Topic Maps is to language. ¾ RDF/OWL is to Aristotle as Topic Maps is to Wittgenstein. http://www.ontopedia.net/pepper/slides/IFI.ppt

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What is this supposed to be? Is it an H or an A?

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H H A A

T EC T z z


The moral is: Fuzziness is a fact. Humans can handle it; machines can’t.

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Different capabilities z

RDF/OWL, to support logic-based inferencing, cannot allow fuzziness –

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OWL ontologies tend to be very stringent and complex –

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Topic Maps ontologies tend to be simple and less formal

OWL has properties for things that Topic Maps doesn’t need –

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Topic Maps, because it is for humans, has to support fuzziness

Some Topic Maps features would be too complex for OWL

In short, they are optimized for different purposes...


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RDF or Topic Maps? z

Do you simply want to encode document metadata? –

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Go straight for Topic Maps because it also supports metadata

Do you want to develop agent-based applications? –

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Topic Maps provides the best combination of flexibility and user-friendliness

Do you want both metadata and subject-based classification? –

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RDF is ideal and you won’t need OWL

Do you want to achieve subject-based classification of content? –

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RDF is more low-level; oriented towards machines Topic Maps is more high-level; oriented towards humans OWL is oriented towards artificial intelligence

Use RDF/OWL ... but if you already have Topic Maps, you’re half way there

Most importantly, whatever you choose, you can always move your data between RDF and Topic Maps, thanks to the RDFTM work…


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RDFTM: Data interoperability z

RDF/Topic Maps Interoperability Task Force –

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Chartered to deliver two documents: –

Survey of Existing Interoperability Proposals

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Guidlines for RDF/Topic Maps Interoperability

Survey published in February 2006 –

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A task force within the Semantic Web Best Practices and Deployment Working Group

http://www.w3.org/TR/rdftm-survey/

Draft guidelines published in June 2006 –

http://www.w3.org/2001/sw/BestPractices/RDFTM/guidelines-20060630.html

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The task force is now disbanded and the work will be finalized by ISO


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Subject-centric computing – a broader perspective


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TM2008 Topic page Emails Documents Web topic pagesmaps

K185

opera OOXML

tm2008

Ψ

bayreuth

http://www.ontopedia.net/pepper/slides/IFI.ppt håkon

LING 2110

Copy PSI

rana

janacek

gambia

keynote

bantu semantics

INF 2820

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Subject-centric file system z

The file system is a hierarchy and that’s a pain –

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z

Trees aren’t expressive enough

WinFS looked like it might change all that –

New data storage and management system announced in 2003

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Didn’t make it into Vista. Seems to have disappeared

Let the new file system be a topic map! –

“Folders” are topics with global identifiers

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User-defined metadata on “folders” (internal occurrences)

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External occurrences

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Related through navigable, typed associations


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Subject-centric operating system z

Now that the file system is a topic map, why not go the whole hog? –

Services to applications for assigning PSIs

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NLP based help for (semi-automatically) categorizing documents

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Ability to extract fragments from the system topic map

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Peer-to-peer features for exchanging fragments with others

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Facilities for context-based virtual merges under user control

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...


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