Analysis of event-based information-seeking tasks
What is an event? Is it the same thing for a journalist as for a stock broker? What aspects of events are relevant to users? EVERBEST aims to provide answers to these questions!
What is an event? Is it the same thing for a journalist as for a stock broker? What aspects of events are relevant to users? EVERBEST aims to provide answers to these questions!
In EVEREBEST we aim to identify optimal event representations for particular information-seeking tasks as well as generalization operators to model underlying information needs.
The end results of the EVERBEST project will be the event-based retrieval and recommendation engine built on top of EventRegistry, an existing news-feeding platform.
Our paper entitled “Detecting and Ranking Conceptual Links between Texts Using an External Knowledge Base” has been accepted for publication at the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016) that will be held in Indianapolis in October. In this work we proposed a novel algorithm for identifying conceptual relatedness between news stories based on an external knowledge base.
Researchers from TakeLab, University of Zagreb and University of Nottingham have successfuly completed the first set of interviews as part of the initial exploratory study on user perception of news and events. The study included interviews with seven general news consumers and 12 information workers (journalists and business analysts).
A two-day EVERBEST kick-off workshop was held @ FER, with the participation of the University of Notthingam, AILab from Josez Stefan Institute, and the hosts, TakeLab FER.