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Lydia Lau, Vania Dimitrova , Dhaval Thakker & Dimoklis Despotakis

Semantically-enriched Intelligent Support to Make Sense of User Generated Content . Lydia Lau, Vania Dimitrova , Dhaval Thakker & Dimoklis Despotakis School of Computing, The University of Leeds, UK. The ImREAL Approach. Augmented Simulated Experiential Learning.

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Lydia Lau, Vania Dimitrova , Dhaval Thakker & Dimoklis Despotakis

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  1. Semantically-enriched Intelligent Support to Make Sense of User Generated Content Lydia Lau, VaniaDimitrova, DhavalThakker & DimoklisDespotakis School of Computing, The University of Leeds, UK

  2. The ImREAL Approach Augmented Simulated Experiential Learning Simulated Environmentfor Learning Learning experience Simulation design Using digital traces with user generated content in social spaces to connectthe learning experience in a virtual learning environment with the ‘real-world’ context and everyday life.

  3. Simulated Environmentfor Learning Focus of this presentation Affective Meta-cognitive Scaffolding Pedagogy – Use Cases Evaluation – User Trials Augmented user modelling Making Sense of Digital Traces Integration Framework

  4. MaSCot Process :Making Senseof CollectiveContent digitaltraces I-CAW Browsing & interaction Semantic query Semantic augmentation AMOn+ & other ontologies Viewpoint exploration ViewS Microscope Focusextraction

  5. Challenge 1 • Structure, link, organise collective content from ill-defined domains (e.g. interpersonal communications). • This involved: • Application and development of Activity Theory as a heuristic tool to understand and represent ill-defined domains. • An underpinning ontology Activity Model Ontology for interpersonal communications (AMOn) and extended it with cultural aspects (AMOn+). http://imash.leeds.ac.uk/ontologies/amon

  6. AMOn+ http://imash.leeds.ac.uk/ontologies/amon/LODE%20outputs/CulturalAspects.owl.htm

  7. Challenge 2 • Exploit semantics to enable intuitive exploration of the collective content. • This involved: • Development of technology and algorithms for aggregating, organising and linking digital traces from multiple sources to support browsing, search and understanding. • A semantic data browser, Intelligent Content Assembly Workbench (I-CAW), with semantic augmentation and query services. • A tool for exploring viewpoints in user generated content - ViewS microscope.

  8. I-CAW: Semantic Browser Focus Concept Facts Eye Contact is Body Language Social Content

  9. Semantic Nudges for Learning Nervous is a negative emotion, you may also look at positive emotions like clam. Click here to see examples with calm.

  10. ViewS: Viewpoint Analysis Clusters show learner attention Hot topics to focus on (e.g. extend feedback) Depth vs breath (abstract vs concrete concepts) WNAffect mental state entities, ALL episodes

  11. Gateway to Comments: WNAffect Example user comments: positive and negative emotions

  12. Challenge 3 • Enable external applications to capitalise on the diversity and richness of collective content. • This involved: • MaSCoT - A loosely coupled infrastructure designed for re-usability of services and collected content.

  13. MaSCoT Framework connecting organising selecting comparing reflecting Interface visualise explore browse dialogue search Reasoning Semantic Linking Enrichment Semantic querying User model update Semantic tagging Dialogue planner Viewpoints engine Nudging engine Data User model Corpora Ontologies Linked data Content

  14. Lessons Learnt Exploratorysearch digitaltraces User modelling digimind.com Rich authentic examples Diversity of content and users Semantic augmentation Linked data & bespoke ontologies Content organisation Semantic nudges User profiles Relevance, reliability Can be taken mistakenly as norm Linked data noisy Methods hard to generalise Need creative ways to link to learning Need rigorous testing

  15. http://www.imreal-project.eu/ Semantically-enriched Intelligent Support to Make Sense of User Generated Content Lydia Lau, VaniaDimitrova, DhavalThakker & DimoklisDespotakis School of Computing, The University of Leeds, UK

  16. How we use semantics to connect to relevant experiences Stage 1: Activity Modelling on Interpersonal Communications Real World Analysis Activity Theory on a Use Case Use Case Activity Model other relevant ontologies Stage 2: Activity Modelling Enrichment using Semantics Real world experiences Multi-layered Activity Modelling Ontology (AMOn) for Interpersonal Communications Logical Encoding Stage 3: Providing Access to Relevant Real World Experiences Semantic Services: Augmentation, Query, Viewpoint Extraction I-CAW Story Boarding

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