Aladdin Shamoug, Stephen Cranefield, & Grant Dick. (2018). Information Retrieval for Humanitarian Crises via a Semantically Classified Word Embedding. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 132–144). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Decision-makers in humanitarian crisis need information to guide them in making critical decisions. Finding information in such environments is a challenging task. Therefore, decision-makers rely on domain experts who possess experience and knowledge from previous humanitarian crises to provide them with the information they need. In this paper, we explore the ability of the existing computing technologies to augment the capabilities of those experts and help decision-makers to make faster and better decisions. Among many computing technologies we have today, word embedding and the semantic web are able to support such augmentation of the domain expert. In this paper, we train a word embedding model using word2vec, transform words and terms from news archive to entities in domain ontology, annotate those entities with their equivalent concepts from upper ontologies, and reason about them using semantic similarity and semantic matching, to represent and retrieve knowledge, and answer questions of interest to decision-makers in humanitarian crises. The approach was evaluated by comparing the use of word embeddings with and without semantic classification for the retrieval of information about the current humanitarian crisis in Syria.
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Anja Van Der Hulst, Rudy Boonekamp, & Marc Van Den Homberg. (2014). Field-testing a comprehensive approach simulation model. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 575–584). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the field tests of a simulation based game aiming at raising awareness and creating a deeper understanding of the dynamics of the comprehensive approach (CA). The setting of this game is that of a failed state where an UN intervention takes place after massive conflict that requires a CA to stabilize the situation. That is, the civil and military actors need to collaborate effectively, taking into account their respective strengths, mandates and roles. Underlying the game is the Go4it CA simulation Model (GCAM2.0). GCAM2.0 was extensively field-tested in eight sessions with about 16 persons each, aiming at assessment of the perceived realism and learning effects. It was found to provide a sufficiently authentic experience to obtain awareness of the CA in novices. With regard to improving the deeper understanding of the dynamics and complexity of the CA, in a cooperation-oriented setting only deeper learning can be reached.
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