1. co-funded by the European Union WeKnowIt Emerging, Collective Intelligence for personal, organisational and social use http://www.weknowit.eu Event Detection Processing and Representation Advances, Future Applications, Challenges Yiannis Kompatsiaris CERTH-ITI

2. co-funded by the European Union Groups Caption Time Low- level User Profile Favs Comms Geo Social network Tags Event Processing in User Generated Content / Social Media / Web 2.0

3. co-funded by the European Union Event Detection Research approaches  Community Detection (Graph-based)  Image clusters based on finding tag-image communities in social network  Graph-based, fast and scalable community detection approach  Time aware user-tag co-clustering  Co-clustering based  Detects on the same time topics and users relevant to the event  LDA probabilistic approach  generalization of Latent Dirichlet Allocation (LDA) approach  Events are indicated by unusual content or annotation that is localized in space and time

4. co-funded by the European Union Results and Applications  User-Genrated maps of Points of Interests  Where there is (was) something interesting happening demo: www.clusttour.gr  Name events by most important tags

5. co-funded by the European Union Time-aware user-tag co-clustering Accesso ries, bags, fashion, Cars, football, holidays, horses, sea, turkey, fashion New York, hat, trousers, fashion, Gucci animals, elephants, nature sea, turkey, bags hats, Gucci fashion, jeans, NY User 1 User 2 User 3 fashionweek, fashion, silk, wool

6. co-funded by the European Union Research for upcoming Events  Bursts detection in networks of tag co-occurences  Event is an emerging context tag cluster  Detect building-up events by updating tag connectivity strenght from user input stream  Monitor “hot topics” related to specified tags Challenges  System response must by within seconds  Fast updates on large scale graph  Alarm triggered when event reaches threshold  Monitor emerging clusters

7. co-funded by the European Union Representation – Event Model F  Based on the foundational ontology DOLCE+DnS Ultralight (DUL) - OWL  Representation for time and space, objects and persons  Mereological, causal and correlative relationships between events  Provides flexible means for  event composition  modeling event causality and event correlation  representing different interpretations of the same event.  Available from :  http://west.uni-koblenz.de/eventmodel/

8. co-funded by the European Union Events Representation - Applications  Monitoring/merging event log files  Explore and visualize large semantically heterogeneous distributed semantic datasets in real-time.

9. co-funded by the European Union Challenges  Granularity of event recognition – trade-off  Few, large, better quality events (e.g. fairs, concerts)  Lots, smaller, noisy events (e.g. birthday parties)  Event naming  Can localize event and display relevant tags, but not always assign simple name (as person would do)  Sparsity of user data  Need large number of geo-localized, timestamped and tagged resources (images) for certain location (e.g.) city and longer time (few years)  Representation  Generating APIs for pattern-based ontologies  Reasoning  Adaptation to domain-specific requirements

10. co-funded by the European Union Thank you! WeKnowIt http://www.weknowit.eu Yiannis Kompatsiaris http://mklab.iti.gr