University of Dublin Trinity College Localisation and Personalisation: Dynamic Retrieval & Adaptation of Multi-lingual Multimedia Content Prof Vincent Wade Knowledge & Data Engineering Research Group Trinity College Dublin Deputy Director CNGL

University of Dublin Trinity College 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 Dublin Trinity College Personalisation: Adapting Queries, Content & Delivery to User’s context… Prior Knowledge & Expertise Cognitive Sytle & Competences Competences Environment Aims and Goals Preferences & Culture Language & Communication Style User

University of Dublin Trinity College 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.....

University of Dublin Trinity College Two examples of Personalisation on the Web from Technology Enhanced Learning Developed by Knowledge & Data Engineering Research Group TCD

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College © VW

University of Dublin Trinity College

University of Dublin Trinity College

University of Dublin Trinity College 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

University of Dublin Trinity College 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 Dublin Trinity College Localisation & Personalisation... a continuum ?? Localisation (Geographic or Population based) Personalisation (Individualised or Community Based) Language Goals Context Preferences Prior Experience Prior Competences National/Cultural Conventions

University of Dublin Trinity College Localisation & Personalisation... A hierarchy ?

University of Dublin Trinity College Example Applications for Multilingual Personalisation  Bulk Localisation (low personalisation, high volume)  Informative Portals (high personalisation, lower volume)  Collaborative Portals / Social Networking (high personalisation, user generated content)

University of Dublin Trinity College EADL 2007 © VW Use Scenarios/Demonstrators Grounding Project Pers Vol Acc Personalised Multilingual Social Networking Personalised Production Content for Informal Learning Bulk Localisation

University of Dublin Trinity 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 Dublin Trinity 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 Dublin Trinity College Simple Example Hercules? Adapted Quer(ies) Educational Context, Hercules Age Project on Stars Hyperlinked document base, Domain Ontology User Model(s), Context Models & Ontologies Dynamically composed, personalised response(s) Content Sources

University of Dublin Trinity College 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

University of Dublin Trinity College EADL 2007 © VW Query Representation Content Indexing Query Generation Focused Crawling Content slicing User Modelling Ontology Control Vocabs. Content Aggregration Content Composition Adaptive Navigation Adaptive Presentation Content Metadata Service Choreography Adaptive Web Service Description Service Behaviour Search Algorithms

University of Dublin Trinity College Language Technologies Query Representation Content slicing Search Algorithms Content Translation Ontology Control Vocabs. Information Extraction Information Mining Text Analysis Linguistic Analysis Content Metadata Grammars Logic

University of Dublin Trinity College Language Technologies 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 Ontology Control Vocabs. Information Extraction Information Mining Text Analysis Linguistic Analysis Content Aggregration Content Composition Adaptive Navigation Adaptive Presentation Content Metadata Service Choreography IR Adaptive Web Grammars Logic Service Description Service Behaviour

University of Dublin Trinity College Simple Example Hercules? Adapted Quer(ies) Educational Context, Hercules Age Project on Stars Hyperlinked document base, Domain Ontology User Model(s), Context Models & Ontologies Dynamically composed, personalised response(s) Content Sources

University of Dublin Trinity College User Digital Content, Translation & Speech Services Multilingual Digital Content Sources & Models Adaptive Portal User Model(s), Context Models & Ontologies Personalisation Architecture

University of Dublin Trinity College 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.....

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University of Dublin Trinity College