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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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Aladdin Shamoug, Radmila Juric, & Shamimabi Paurobally. (2012). Ontological reasoning as a tool for humanitarian decision making. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: We propose an OWL/SWRL enabled ontological environment which can play a role in reporting and decision making in Humanitarian Crises (HC). We use (5WH): WHO, WHAT, WHERE, WHEN, WHY and HOW, as the main vehicle for gathering information for decision making. We implement the semantics of (5WH) through OWL models and perform reasoning with SWRL rules, in order to support decision making and create more efficient Humanitarian Response (HR). Our case study shows the feasibility of the proposal and its outcome. © 2012 ISCRAM.
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