Tiddler customised publishing based on xml profiles and xml data sources
Download
1 / 24

Tiddler: Customised publishing based on XML profiles and XML data sources - PowerPoint PPT Presentation


  • 314 Views
  • Uploaded on

Tiddler: Customised publishing based on XML profiles and XML data sources . François Paradis , C é cile Paris, Anne-Marie Vercoustre, Stephen Wan, Ross Wilkinson, MingFang Wu. CSIRO Mathematical and Information Sciences. Outline. Motivation Examples Current approaches Our approach

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Tiddler: Customised publishing based on XML profiles and XML data sources ' - Pat_Xavi


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Tiddler customised publishing based on xml profiles and xml data sources l.jpg

Tiddler: Customised publishing based on XML profiles and XML data sources

François Paradis, Cécile Paris,

Anne-Marie Vercoustre, Stephen Wan, Ross Wilkinson, MingFang Wu

CSIRO Mathematical and Information Sciences


Outline l.jpg
Outline data sources

  • Motivation

  • Examples

  • Current approaches

  • Our approach

  • How it works?

  • Analysis and Conclusion


Motivation why customised publishing l.jpg
Motivation: data sources Why customised publishing?

  • Too much information: people want less information but more relevant to their need, knowledge, or task

  • On different devices at different times: paper, Web, WAP

  • (To build customer relationship)


Examples l.jpg
Examples data sources

  • Customised Travel Guides

    • Depending on who (preferences), where to go , when to go,

    • Depending on when/where to use it

  • Corporate brochures

    • Depending on who you are and your current interest(s)


Current techniques l.jpg
Current techniques data sources

  • Distinct versions of manually crafted documents: one for printing, one for the Web. No personalisation

    Word -> HTML; Latex -> HTML; HTML-> WAP

  • Information Retrieval: personalisation through queries, synthesis of the results; no much coherence

  • Document generation from database queries and different stylesheets: coherence but not high level semantic of the resulting document. Limited type of sources

  • Document generation using NL techniques: relies on theavailability of knowledge base in appropriate format


Tiddler approach l.jpg
Tiddler approach data sources

  • Exploits both language generation and IR-document synthesis approaches:

    • Coherence preserved

    • Wide variety of data sources (including web pages) accessible

  • Dynamically plan documents

  • Customise information using user models

  • Generate documents for multiple media types (Paper, Palm Pilots, Web browsers, Mobile Phones)


System architecture l.jpg

Data Sources data sources

eg. Databases,

Web

User Model

Information Need

Media Need

System Architecture

NORFOLK

Virtual Document Planner

Content Planner

Discourse

Rules

Presentation Planner

Surface Generator

Customised

Documents


User model l.jpg
User Model data sources

  • Include

    • preferences,

    • information need,

    • Context (device)

    • historic

  • Collected via a G.U.I. Interface

  • Used to:

    • customise information to user

    • determine layout and content detail depending on media

    • encapsulate some Users’ Goal

      • Goal is about information need

      • Virtual Document Planner resolves goal using Planning techniques


Input user model l.jpg
Input: User Model data sources

  • Name: Zoe

  • Medium: Palm Pilot

  • Destination: Melbourne

  • Date: 1 June-15 June 2001

  • Activities: Cycling, Opera, Major Mitchell

  • Travel Information:

    Accommodation (backpacker)


Xml representation l.jpg
XML Representation data sources

<usermodel xml:space="preserve" id="Zoe">

<name>Zoe</name>

<destination> Melbourne</destination>

<date>

<start> <day>01</day> <month>June</month> <year> 2001</year> </start>

<end> <day>15</day> <month>June</month> <year>2001</year> </end>

</date>

<wants>

<activities>Cycling, Opera, Major Mitchell </activities>

<accommodation> <accom_range>Backpacker</accom_range>

</accommodation> <events/>

</wants>

<medium>palm-pilot</medium>

….

</usermodel>


Output palm pilot version l.jpg

General data sources HotelsTo DoContacts

Facts at a glance

Population: 3.3 million

Country: Australia

Time Zone: GMT/UTC plus 10 hours

Telephone Area Code: 03

Events

Major Mitchell

Output: Palm Pilot Version


Output web version l.jpg
Output: Web Version data sources


Virtual document planner overview 1 l.jpg
Virtual Document Planner: data sources Overview 1

The Virtual Document Planner:

  • uses Planning Techniques:

    • Goal achieved by finding subgoals that satisfy it

    • Subgoals are linked by rhetorical relations

    • Subgoals satisfied by:

      • other decomposable subgoals

      • primitive subgoals


Virtual document planner overview 2 l.jpg
Virtual Document Planner: data sources Overview 2

The Virtual Document Planner:

  • produces a branching tree structure:

    • Node = information need goal

    • Nodes in branches = subgoals

    • Nodes linked by rhetorical relations

  • Subgoals and Goals represent:

    • content selection

    • presentation decisions


Slide15 l.jpg

Tree for data sources

Zoe Example

Enablement

Preparation

Background

Title, Source

Joint

Further Contact

General Information

Joint

Hotels

Opera

Cycling

Major Mitchell


Virtual document planner sub stages l.jpg
Virtual Document Planner: data sources Sub-stages

Three substages:

  • The Content Planner

  • The Presentation Planner

  • The Surface Generator


Virtual document planner sub stage 1 l.jpg
Virtual Document Planner: data sources Sub-stage 1

The Content Planner:

  • uses Goal Planning

  • produces a tree structure

    • nodes = document content

    • Branches = rhetorical relations that may be realised with discourse markers


Virtual document planner sub stage 2 l.jpg
Virtual Document Planner: data sources Sub-stage 2

The Presentation Planner:

  • Leaves of the tree = chosen content

  • Leaves expanded with layout mark-up of document

  • Mark-up depends on document organisation

  • Customised for particular media type.


Virtual document planner sub stage 3 l.jpg
Virtual Document Planner: data sources Sub-stage 3

The Surface Generator:

  • Dependent on medium

  • Content and layout mark-up are mapped to:

    • text

      • XML

      • HTML

      • WML

      • Natural Language

    • graphics

      • pictures

      • tables

      • lists


Data sources l.jpg
Data Sources data sources

  • Norfolk technology:

    • provides interface between:

      • Virtual Document Planner

      • Data sources

  • Data Sources originate from:

    • corporate data bases

    • existing web pages of known layout (wrapping)

  • Data Sources can be:

    • static: Norfolk retrieves content in advance -> XML

    • dynamic: Norfolk retrieves content as needed by Virtual Document Planner


Why are dynamic documents useful l.jpg
Why are data sources Dynamic Documents useful?

A document can:

  • be composed using most up-to-date information

  • customise information to user

  • tailor content to particular query

  • tailored to a particular media


What are the limitations of current dynamic pages l.jpg
What are the limitations of current dynamic pages? data sources

Dynamic pages are often:

  • statically planned with templates and stylesheets

    • Templates grow exponentially in number as document becomes more flexible

  • represented in program language code

    • makes maintenance more difficult

  • limited to filtering at document level for customisation

  • required to maintain separate templates for different media


Conclusions 1 l.jpg
Conclusions (1) data sources

Tiddler Advantages:

  • Easier to maintain because

    • Documents use goal planning, not template based

    • Document Rules not in a program language code

  • Customisation filters and uses relevant information from parts of documents

  • Information can be gathered from multiple sources

  • Documents for different media are generated from the same document skeleton

    • Only need to update the skeleton


Conclusions 2 l.jpg
Conclusions (2) data sources

Future Work:

- Reasoning about the discourse to provide feedback/explanations

- Dynamic and complex user model to deal with history of information delivery

- Complex user model to build customer relationship


ad