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Enhanced Mobile TV Experience with Semantic Web and Web 2.0

Explore how MyTV 2.0 leverages Semantic Reasoning and Web 2.0 for Mobile TV, enabling personalized content delivery, social interactions, and semantic annotations for an enriched viewing experience.

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Enhanced Mobile TV Experience with Semantic Web and Web 2.0

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  1. MyTV2.0: SemanticReasoning and Web 2.0 for Mobile TV Yolanda Blanco Fernández yolanda@det.uvigo.es ISCE 2008 Algarve, Portugal

  2. Introduction • Mobile TV (m-TV) is a step further in the mobile phones use. • Access to multimedia contents over 2G/3G networks. • Move to broadcasting technologies: • DVB-H (Digital Video Broadcasting – Handheld). • Short times to watch m-TV  Filtering techniques to select interesting contents are needed. • MyTV 2.0: Web 2.0 & Semantic Web

  3. myTV 2.0 • Web 2.0: • Collaboration and knowledge sharing among active users. • User interact in social networks: creation and tagging of contents. • Semantic Web: • Semantic annotations (metadata)  meaning of information  interoperability among machines. • Discovery of semantic relationships among annotated resources by reasoning about knowledge in an ontology. • Concepts and relationships typical in the domain of application.

  4. Architecture of MyTV 2.0 • Roles: content providers, m-TV providers, m-TV users. • Centralized architecture: • m-TV providers broadcast content throughout DVB-H network. • m-TV subscribers consume content in a personalized way. • Cooperative architecture: • Besides, m-TV users record, tag and upload contents over 2G/3G network. • Contents from m-TV provider: • Formally labeled contents  content provider • Informally tagged contents  subscriber

  5. content providers m-TV provider TVContent … DVB-H network Documentaries Entertainment Shows Cinema Programs 2G/3G network Architecture of MyTV 2.0 (2) feedback Group filtering Individual filtering TVOntology tagging m-TV users contentupload rating Folksonomy m-TV users

  6. Two-phase Filtering Process: Advantages • The receiver is a limited-resource platform. • Recommendations from the beginning. • Better resources at the m-TV receiver  better recommendation. • Structure an characteristics of audience. • Content planning. • Saving recommendation process at the headend. • Only once for every stereotype.

  7. The Headend • Subscribers role: • Review content, define tags, upload content. • From this information: • TVOntology is enriched (emergent folksonomy) • Fields TV-Anytime: keywords, review. • Decision about content to be broadcast: • User-uploaded content: FLUTE carrousel. • m-TV content: FLUTE or RTP.

  8. TheHeadEnd: GroupFiltering Content Service Delivery Network Service Content Creator Content Provider Content Aggregator Service Operator DVB-H IPDC Network Operator Content Metadata Content SSG1 ESG SSGn Personalization layer • Recommender system: AVATAR • Semantic reasoning about TVOntology • Create SSGs (Social Service Guides). • - Specific ESG for stereotype • Broadcast SSGs: • - Stereotype  IP address Recommender OntologyIndividuals (Users, contents, tags) User/social groups Manager User/stereotype Manager Subscription Valuation Tagging Content upload 2G/3G Return Channel Mobile Operator Return Channel Service Operator Social groups changes

  9. NewsContent user’s profile DOI DOI Business Sports DOI DOI Stock exchange Tennis Football DOI SSG2 SSG1 Bootstrap ESG The Receiver: Individual Filtering OSGi Registry Presentation logic • Content access: • RTP content (on-line • FLUTE content • Personalization logic: • Lite-AVATAR • Limited-resource devices. • Compute relevance of contents for user. • Presentation logic: • myTVContents  off-line contents • myTVGuide  on-line contents myTVGuide myTVContent Personalization logic Recommender Voting Notifier Valuation Sender Ontology Download News Ontology DOI Requester RealTime Access Offline Access OSCAR framework Content access Local resources DVB-H API

  10. Final Discussion • m-TV change the viewers’ routines  TV consumption at short idle intervals. • Personalized virtual channels by filtering broadcast contents as per users’ interests. • DVB-H for broadcast • 2G/3G network for return channel. • Semantic Web and Web 2.0 • Inference of knowledge from domain ontologies. • Social networks: user-generated content, participation, recommendation, folksonomies, tags, sharing.

  11. Thank you for your attention!

  12. MyTV 2.0: SemanticReasoning and Web 2.0 for Mobile TV Yolanda Blanco Fernández yolanda@det.uvigo.es ISCE 2008 Algarve, Portugal

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