Tiddler: Customised publishing based on XML profiles and XML ...

1. Tiddler: Customised publishing based on XML profiles and XML data sources  François Paradis, Cécile Paris, Anne-Marie Vercoustre, Stephen Wan, Ross Wilkinson, MingFang Wu

2. Outline Motivation Examples Current approaches Our approach How it works? Analysis and Conclusion

3. Motivation: 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)

4. Examples 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) Bring the India Lonely Planet Guide, and some glossy brochure from a travel agency. Bring CSIRO flyers. Bring the India Lonely Planet Guide, and some glossy brochure from a travel agency. Bring CSIRO flyers.

5. Current techniques 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 the availability of knowledge base in appropriate format Can be see as three generations of document generation. Many variants of this. Even in generation 1, there where tools for generating personalised documents, e.g. personalised maintenance manuals (SGML based)Can be see as three generations of document generation. Many variants of this. Even in generation 1, there where tools for generating personalised documents, e.g. personalised maintenance manuals (SGML based)

6. Tiddler approach 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)

7. System Architecture

8. User Model 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

9. Input: User Model Name: Zoe Medium: Palm Pilot Destination: Melbourne Date: 1 June-15 June 2001 Activities: Cycling, Opera, Major Mitchell Travel Information: Accommodation (backpacker)

10. XML Representation The model is so far very application dependant. We need to model: long term (user “preference”) short term: user query and context History The model is so far very application dependant. We need to model: long term (user “preference”) short term: user query and context History

11. Output: Palm Pilot Version

12. Output: Web Version

13. Virtual Document Planner: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 Ask Cecile for examples of rhetorical relations.Ask Cecile for examples of rhetorical relations.

14. Virtual Document Planner: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

16. Virtual Document Planner: Sub-stages Three substages: The Content Planner The Presentation Planner The Surface Generator

17. Virtual Document Planner: 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

18. Virtual Document Planner: 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.

19. Virtual Document Planner: 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

20. 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

21. Why are 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

22. What are the limitations of current dynamic pages? 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

23. Conclusions (1) 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

24. Conclusions (2) 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 The advantages of the use of discourse model, which is that it allows us to reason about the discourse, so it makes the feedback with users more meaningful. (we know why we generated a piece of discourse, and can explain it to the user too; we know that we've explained something to the user already, so don't need to explain it again, etc.) Or, use the discourse model to keep track of what the user has already been presented (so that we don't repeat information). The advantages of the use of discourse model, which is that it allows us to reason about the discourse, so it makes the feedback with users more meaningful. (we know why we generated a piece of discourse, and can explain it to the user too; we know that we've explained something to the user already, so don't need to explain it again, etc.) Or, use the discourse model to keep track of what the user has already been presented (so that we don't repeat information).