co-funded by the European Union

WeKnowItEmerging, Collective Intelligence for personal,

organisational and social use http://www.weknowit.eu

Event Detection Processing and RepresentationAdvances, Future Applications, Challenges

Yiannis KompatsiarisCERTH-ITI

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

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

co-funded by the European Union

Results and Applications User-Genrated maps of Points of Interests

Where there is (was) something interesting happeningdemo: www.clusttour.gr

Name events by most important tags

co-funded by the European Union

Time-aware user-tag co-clustering

Accessories, 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

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

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/

co-funded by the European Union

Events Representation - Applications

Monitoring/mergingevent log files

Explore and visualize largesemantically heterogeneousdistributed semantic datasetsin real-time.

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

co-funded by the European Union

Thank you!

WeKnowIthttp://www.weknowit.eu

Yiannis Kompatsiarishttp://mklab.iti.gr