University of Maryland

[Call For Papers] SMILE 2013 : Social Media and Linked Data for Emergency Response Workshop

February 18th, 2013 by

* Social Media and Linked Data for Emergency Response Workshop *

Co-located with the 10th Extended Semantic Web Conference – May 26-30, 2013 at Montpellier, France

Submission Deadline: March 4, 2013
Acceptance Notification: April 1, 2013
Camera-Ready: April 15, 2013

Emergencies require significant effort in order for emergency workers and the general public to respond effectively. Emergency Responders must rapidly gather information, determine where to deploy resources and make prioritization decisions regarding how best to deal with the emergency. Good situation awareness [1] is therefore paramount to ensure a timely and effective response. Thus, for an incident to be dealt with effectively, citizens and responders must be able to share reliable information and help build an understanding of the current local and global situation and how this may evolve over time [2]. Information available on Social Media is increasingly becoming a fundamental source for Situation Awareness. During a crisis, citizens share their own experiences, feelings and often, critical local knowledge. Integrating this information with Linked Open Data, (such as geographic or demographic data) could greatly enrich its value to better prevent and respond to disasters and crisis.

These characteristics make the automation of the intelligence gathering task hard, especially when considering that (i) documents must be processed in (near) real-time and (ii) the relevant information may be in the long-tail of the distribution, i.e. mentioned very infrequently. Common techniques for extracting information from text have been applied to Social Media content with alternate success. For e.g., Named Entity Recognition (NER) techniques that extract semantic concepts have been shown to perform poorly on short and noisy social media content [3]. While annotation services and APIs are a highly stimulating research direction for understanding the content and context of social media streams, the aggregation and integration of multi-dimensional datasets, from different domains and large volumes of data still pose a significant technical challenge to development in this area.

Understanding and acting upon large–scale data of different nature, provenance and reliability is a significant knowledge management challenge. Decision-support and visualization techniques must be developed to enable data exploration and discovery for crisis management purposes. Social challenges involved in exploiting social media and Linked Open Data for crisis situations include: credibility, accountability, trustworthiness, privacy, authenticity and provenance of information.

SMILE aims to gather innovative approaches for exploitation of social media using semantic web technologies and linked data for emergency response and crisis management. The workshop would cover advancements in the relevant areas. SMILE aims to bring together expertise from three research areas:

– Semantic Web and Linked Data;
– Social Sciences;
– Emergency Response and Crisis Management;

The following topics are of special interest to SMILE:

– Semantic Annotation, for understanding the content and context of social media streams
– Integration of Social Media with Linked Data
– Interactive Interfaces and visual analytics methodologies for managing multiple large-scale, dynamic, evolving datasets.
– Stream reasoning and event detection
– Social Data Mining
– Collaborative tools and services for Citizens, Organisations, Communities
– Privacy, ethics, trustworthiness and legal issues in the Social Semantic Web
– Use case analysis, with specific interest for use cases that involve the application of Social Media and Linked Data methodologies in real-life scenarios

Applied in the context of:

– Crisis and Disaster Management
– Emergency Response
– Security and Citizen journalism

Full research papers, up to 12 pages
Short papers and position papers, up to 6 pages
Posters and Demonstrations, 4 pages with the description of the application and a link to a live online demo (for demonstrations).

More details at

– Dr. Vitaveska Lanfranchi, University of Sheffield, UK
– Suvodeep Mazumdar, University of Sheffield, UK
– Dr. Eva Blomqvist, Linköping University, Sweden
– Dr. Christopher Brewster, Aston University, UK


Neil Ireson, University of Sheffield, United Kingdom
Dr. Sam Chapman, K-Now, United Kingdom
Amparo Elizabeth Cano Basave, KMI, United Kingdom
Dr. Rodrigo Carvalho, K-Now, United Kingdom
Andrea Varga, University of Sheffield, United Kingdom
Dr. Irina Temnikova, University of Wolverhampton, United Kingdom

More details at