Emergencies require signi cant e ort in order for emergency workers and the general public to respond e ectively. 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 e ective response. Thus, for an incident to be dealt with e ectively, 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 Data, such as geographic or demographic data, could greatly enrich its value to better prevent and respond to disasters and crises. Analysing, modelling and integrating social media content and Linked Data presents signi cant technical as well as social challenges. Social data is: (i) high in volume, rapidly changing and constantly increasing, (ii) often duplicated, incomplete, imprecise and potentially incorrect; (iii) textual content may be written in informal style (i.e., short, unedited and conversational), thus much less grammatically bounded and containing extensive use of shorthand, symbols (e.g., emoticons), misspellings etc.; (iv) generally concerning the short-term zeitgeist; and (v) covering every conceivable domain. For this the workshop on Social Media and Linked Data for Emergency Response (SMILE2014) called for papers on innovative approaches for exploitation of social media and Linked Data for emergency response and crisis management using semantic web technologies. To address this the proceedings include papers ranging from vehicle routing to crowd-sourcing, social media mining and visualization.
SMILE2014 Chairs