University of Dublin Trinity College Localisation and Personalisation: Dynamic Retrieval & Adaptation of Multi-lingual Multimedia Content Prof Vincent

University of DublinTrinity College

Localisation and Personalisation:

Dynamic Retrieval & Adaptation of Multi-lingual Multimedia Content

Prof Vincent WadeKnowledge & Data Engineering Research Group

Trinity College DublinDeputy Director CNGL


Localisation & Personalisation

Localisation• process of adapting content for a specific region

or language by adding locale-specific components and translating text. (Wikipedia)

Personalisation • adapting multimedia content and services

dynamically to suit an individual goals, users’ preferences, context, prior knowledge etc....

University of DublinTrinity College Personalisation:

Adapting Queries, Content & Delivery to User’s context…

Prior Knowledge & ExpertisePrior Knowledge & Expertise

Cognitive Sytle &Cognitive Sytle &

CompetencesCompetences

EnvironmentEnvironment

Aims and GoalsAims and Goals

Preferences & Preferences &

CultureCulture

Language Language

& Communication & Communication

StyleStyle

User


Personalisation in Today’s Web Applications

Technology Enhanced Learning ‘Kiosks’ and Information Portals Tourists Guides, ‘Location Aware’ applications Museums eCommerce (retail portals)

... But the state of the art is way beyond this .....


Two examples of Personalisation on the Web

fromTechnology Enhanced Learning

Developed by Knowledge & Data Engineering Research Group

TCD


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Motivation: from Localisation to Personalisation

‘One size doesn’t fit all’!• Different people have different languages, cultural sensitivities,

information needs, likes, preferences, skills, abilities

• Are in different locations, using different devices, with different connectivity

• Are in different circumstances, using service for different reasons ……

Large variety of Users, very variable circumstances, large ‘hyper’space of content


What is Adaptive Personalisation of Digital Content ?

“to achieve a more effective, efficient and satisfying user experience

By offering content, activities and collaboration,

adapted to the specific needs and influenced by specific preferences and context of the person,

based on the sound presentational strategies”

University of DublinTrinity College Localisation & Personalisation

... a continuum ??

Localisation

(Geographic or Population based)

Personalisation

(Individualised or Community Based)

Language Goals

Context

Preferences

Prior ExperiencePrior Competences

National/CulturalConventions

University of DublinTrinity College Localisation & Personalisation

... A hierarchy ?


Example Applicationsfor Multilingual Personalisation

Bulk Localisation (low personalisation, high volume)

Informative Portals (high personalisation, lower volume)

Collaborative Portals / Social Networking (high personalisation, user generated content)


EADL 2007 © VW

Use Scenarios/Demonstrators Grounding Project

Pers

Vol

AccPersonalised Multilingual Social Networking

PersonalisedProduction Contentfor InformalLearning

Bulk Localisation

University of DublinTrinity College Key Research Goals and Activities

for Personalised Multilingual Digital Content

Dynamically adapt user queries

Automatically generation metadata &semantic (subject) domain models

Analysis content to generate content and subject metadata and support content slicing;

Adapt & dynamically compose presentations and narratives

Validate and evaluate using industrial content & future scenarios

Improve retrieval relevance, accuracy and impact;

Enable reasoning for adaptive (personalised) hypermedia content

Enable dynamic adaptive multilingual hypermedia composition

Enhance user experience, cognitive comprehension and application

Provide evidence based research results & prove impact an application of research

University of DublinTrinity College Personalisation in CNGL:

Key Research Areas Query Adaptation

• Personalisation of multilingual, text and speech based queries

Automated Content and Subject analysis• Automated content reasoning• Automated Context reasoning• Automated and semi automated content slicing for reuse

Dynamic Personalised Composition• Dynamic Multilingual composition of personalised content (text

& speech synthesis)

University of DublinTrinity CollegeSimple

Example

Hercules? Adapted Quer(ies)

Educational Context, HerculesAge 10-12Project on Stars

Hyperlinked

document base,

Dom

ain Ontology

User Model(s), Context Models & Ontologies

Dynamically composed, personalised response(s)

ContentSources


EADL 2007 © VW

Content visualisation

Query Representation

Statistical Modelling (of content)

Content Indexing

Query Generation

Classification

Focused Crawling

Content slicing

User Modelling

Search Algorithms

Link Counting

Content Translation

IR

Personalisation: Multiple Areas of Computer Science


EADL 2007 © VW


Content Indexing

Query GenerationFocused Crawling

Content slicing

User Modelling

OntologyControl Vocabs.

Content AggregrationContent Composition

Adaptive Navigation

Adaptive Presentation

Content Metadata

Service Choreography

Adaptive Web

Service Description

Service Behaviour

Search Algorithms


Language Technologies


Content slicingSearch Algorithms

Content Translation OntologyControl Vocabs.

Information Extraction

Information MiningText Analysis

Linguistic Analysis

Content Metadata

Grammars

Logic


Language Technologies

Content visualisation


Statistical Modelling (of content)

Content Indexing

Query Generation

Classification

Focused Crawling

Content slicing

User Modelling

Search Algorithms

Link Counting

Content Translation OntologyControl Vocabs.

Information Extraction

Information MiningText Analysis

Linguistic Analysis

Content AggregrationContent Composition

Adaptive Navigation

Adaptive Presentation

Content Metadata

Service Choreography

IR

Adaptive Web

Grammars

Logic

Service Description

Service Behaviour

University of DublinTrinity CollegeSimple

Example

Hercules? Adapted Quer(ies)

Educational Context, HerculesAge 10-12Project on Stars

Hyperlinked

document base,

Dom

ain Ontology


Dynamically composed, personalised response(s)

ContentSources


User

Digital Content, Translation &Speech Services

Multilingual Digital ContentSources & ModelsAdaptive Portal


Personalisation Architecture


Conclusions Personalisation research currently does not

leverage:• Localisation research and know-how• Language (text) analytics• Language Translation techniques

BUT• All will be needed to different degrees as we move

to next generation information systems .....


Thank you ... any questions ...


Documents

University of Dublin Trinity College Localisation and Personalisation: Dynamic Retrieval & Adaptation of Multi-lingual Multimedia Content Prof Vincent