Records |
Author |
Grégoire Burel; Lara S. G. Piccolo; Kenny Meesters; Harith Alani |
Title |
DoRES -- A Three-tier Ontology for Modelling Crises in the Digital Age |
Type |
Conference Article |
Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
Volume |
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Issue |
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Pages |
834-845 |
Keywords |
Crisis Ontology; Situation Awareness; Emergency Model; Events; Reports |
Abstract |
During emergency crises it is imperative to collect, organise, analyse and share critical information between individuals and humanitarian organisations. Although dierent models and platforms have been created for helping these particular issues, existing work tend to focus on only one or two of the previous matters. We propose the DoRES ontology for representing information sources, consolidating it into reports and then, representing event situation based on reports. Our approach is guided by the analysis of 1) the structure of a widely used situation awareness platform; 2) stakeholder interviews, and; 3) the structure of existing crisis datasets. Based on this, we extract 102 dierent competency questions that are then used for specifying and implementing the new three-tiers crisis model. We show that the model can successfully be used for mapping the 102 dierent competency questions to the classes, properties and relations of the implemented ontology. |
Address |
Knowledge Media Institute (KMi), The Open University, Milton Keynes, United Kingdom; Centre for Integrated Emergency Management (CIEM), University of Agder Kristiansand, Norway |
Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Albi, France |
Editor |
Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
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Medium |
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Track |
New Technologies for Crisis Management |
Expedition |
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Conference |
14th International Conference on Information Systems for Crisis Response And Management |
Notes |
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Approved |
no |
Call Number |
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Serial |
2069 |
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Author |
Grégoire Burel; Harith Alani |
Title |
Crisis Event Extraction Service (CREES) – Automatic Detection and Classification of Crisis-related Content on Social Media |
Type |
Conference Article |
Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
Volume |
|
Issue |
|
Pages |
597-608 |
Keywords |
Event Detection, Word Embeddings, Deep Learning, Convolutional Neural Networks, API |
Abstract |
Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings. |
Address |
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Corporate Author |
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Thesis |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
Notes |
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Approved |
no |
Call Number |
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Serial |
2134 |
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